diff --git a/README.md b/README.md index 917bc74730331c7cd5ee3551f3fe1b60f8a880b6..197a1f13af72f051e1178efd61e7073c06ef1cce 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,12 @@ license: apache-2.0 language: [en, es, it, fr, de, vi] library_name: litert pipeline_tag: text-to-speech -tags: [text-to-speech, tts, litert, onnx, pytorch, multilingual, expressive-tts, duration-flow] +tags: [text-to-speech, tts, litert, onnx, coreml, coreai, pytorch, multilingual, expressive-tts, duration-flow] --- # Scylla's Band v3 -Release **`v3-20261104`**. The [Scylla's Band runtime](https://github.com/lowkeytea/scyllasband) supports this release from version 3.0.0. The manifest declares graph contract `scyllasband_measured_controls_v3`; runtimes written for v2 releases cannot run these graphs and keep downloading their v2 tags. +Release **`v3-20261104`**. The [Scylla's Band runtime](https://github.com/lowkeytea/scyllasband) supports this release from version 3.0.0. Tag `v3-20261104-apple` adds Core ML and Core AI bundles of the same model; the runtime downloads from `v3-20261104-apple`. The manifest declares graph contract `scyllasband_measured_controls_v3`; runtimes written for v2 releases cannot run these graphs and keep downloading their v2 tags. This release follows `v3-20261103` with a distilled pitch predictor: it makes 4 network passes per passage instead of 16, and the other models are unchanged. @@ -24,7 +24,7 @@ python -m scyllasband speak --voice ariadne --language en_us --intensity 1 --pac -o hello.wav "It is a pleasure to meet you." ``` -`--flavor litert` or `--flavor onnx` chooses the bundle, installed under `scyllasband/models/`. Downloads pin a release tag. +The default download is the Core ML bundle on an Apple silicon Mac and the LiteRT bundle elsewhere, installed under `scyllasband/models/`. A Mac that can build apps for iOS, iPadOS or visionOS 27 is also offered the Core AI bundle those apps use. `--flavor coreml`, `--flavor coreai`, `--flavor litert` or `--flavor onnx` choose explicitly. Downloads pin a release tag. ## Controls @@ -68,6 +68,34 @@ The flow and the pitch predictor are distilled for few-step sampling: each was t Long text is spoken as passages within the trained target lengths (64 to 420 latent frames, about 1.4 to 9 seconds): a short first passage so audio starts quickly, then passages as long as the text allows, cut at a sentence end, else a clause, else between words, and measured with the requested delivery. Each passage sees the surrounding text as context and continues from the acoustics of the one before it, and consecutive passages are decoded as one continuous waveform. Durations are predicted deterministically; different controls can alter them. +## Performance + +Measured with the default samplers (flow and pitch predictor: Euler, 4 steps each) through the native runtime: `scyllasband_speak` from [libscyllasband](https://github.com/lowkeytea/scyllasband/tree/main/libscyllasband) on the Mac and Android, and the [iOS sample](https://github.com/lowkeytea/scyllasband/tree/main/examples/ios)'s benchmark mode on the iPhone. The text is three sentences, 10.3 s of audio: "The last train leaves at midnight, so pack light. Bring a warm coat, and don't forget the map. If we miss it, we walk, and nobody wants to walk that far in the rain." (voice Scylla, seed 7, the voice's own delivery). + +- **First run**: from launch to first audio on the first launch after installing the bundle. Core ML compiles and Core AI specializes the graphs for the device then (Core AI caches them until the next OS update); LiteRT writes its packed weights to a cache file beside each graph. +- **Cold start**: from launch to first audio on a later launch, including loading the bundle. +- **Warm first audio**: from a request to its first audio in a process that has already spoken (median of three requests). +- **Real-time speed**: seconds of audio generated per second when warm, rounded. + +| Device | Bundle | Compute | First run | Cold start | Warm first audio | Real-time speed | +| --- | --- | --- | --- | --- | --- | --- | +| MacBook Pro, M5 Max (macOS 27) | Core ML | GPU (default) | 2.73 s | 1.34 s | 30 ms | ~170× | +| | Core ML | CPU | 2.17 s | 0.33 s | 221 ms | ~20× | +| | Core AI | GPU (default) | 5.80 s | 0.46 s | 32 ms | ~150× | +| | Core AI | Neural Engine | 34.2 s | 0.60 s | 316 ms | ~20× | +| | Core AI | CPU | 3.54 s | 0.93 s | 844 ms | ~5.0× | +| | LiteRT | CPU | 0.41 s | 0.30 s | 105 ms | ~34× | +| | ONNX Runtime | CPU | 0.88 s | 0.85 s | 216 ms | ~21× | +| iPhone 17 Pro Max (iOS 27.2) | Core ML | GPU (default) | 15.5 s | 1.79 s | 77 ms | ~65× | +| | Core ML | CPU | 2.87 s | 0.49 s | 294 ms | ~15× | +| | Core AI | GPU (default) | 18.5 s | 0.59 s | 70 ms | ~68× | +| | Core AI | Neural Engine | 8.00 s | 0.74 s | 367 ms | ~17× | +| | Core AI | CPU | 7.58 s | 1.16 s | 1010 ms | ~4.1× | +| Galaxy Z Fold 8, Snapdragon SM8850 (Android 17) | LiteRT | CPU | 0.81 s | 0.52 s | 120 ms | ~27× | +| | ONNX Runtime | CPU | 1.85 s | 1.89 s | 461 ms | ~9.2× | + +The GPU is the default on Apple platforms. The Neural Engine placement runs the flow and the pitch predictor on the Neural Engine and the other graphs on the CPU, for apps that speak in the background, where iOS does not allow GPU work. Core ML reloads its graphs on every launch, which accounts for most of its cold start on the GPU. Each passage runs the pitch predictor 4 times, then the flow 4 times. Against `v3-20261103`, whose pitch predictor ran 16 times, warm first audio is 30–45 % shorter on every device and bundle (12–13 % with the Neural Engine placement). + ## Voices and language coverage Fourteen voices: Ariadne, Felix, Gwen, Ink, Kincade, Max, Orpheus, Poppy, Priya, Rex, Scylla, Stone, Thistle, Tuesday. @@ -76,6 +104,8 @@ All fourteen have Spanish (`es`), Italian (`it`), French (`fr`), German (`de`) a ## Artifacts +- `coreai/` (178 MB): Core AI bundle for apps on iOS, iPadOS and visionOS 27 (`.aimodel` assets); it also runs on macOS 27, where the runtime uses the Core ML bundle by default. INT8 weights per output channel with FP16 compute; latent buckets of 128, 256, 384, 512, 768 and 1024 frames are functions of one asset sharing weights. The flow and the pitch predictor also run accurately on the Neural Engine (`controls.coreai.neural_engine_assets`), which is the placement for apps that speak in the background; the GPU is the default. +- `coreml/` (163 MB): Core ML bundle for Apple silicon with iOS 18 / visionOS 2 / macOS 15 and later (compiled `.mlmodelc` assets), with INT8 weights and FP16 compute; GPU by default, CPU in background apps. - `litert/` (229 MB): LiteRT bundle for Linux, Windows, Android and Intel Macs. Dynamic-range INT8 weights on the fully connected, convolution and embedding layers; the vocoder's depthwise convolutions stay in full precision, which keeps them on XNNPACK. Each size is a signature of one model file, sharing weights: latent buckets every 64 frames up to 512 (then 768 and 1024), flow graphs without the 96 prefix frames for a request's first passage (up to 448 frames), and narrower duration, span-context and pitch-context (128 and 256 phones) and G2P (32–256 tokens) widths beside the full ones. - `onnx/` (291 MB): ONNX Runtime bundle with dynamic INT8 weights in the duration, context, pitch and flow transformers; the vocoder stays in full precision. Latent buckets of 128, 256, 384, 512, 768 and 1024 frames share one weight file. - `webgpu/` (291 MB): ONNX Runtime bundle for ONNX Runtime Web's WebGPU backend. Transformer weights are INT8 with float compute (`MatMulNBits`), which runs entirely on the GPU; the vocoder stays in full precision. @@ -94,6 +124,7 @@ Release validation separates graph accuracy, frontend/timing behaviour and perce - The distilled pitch predictor at 4 Euler steps compared with the undistilled predictor at 8 Heun steps (release `v3-20261103`) by blind listening on paired lines across the voices and languages, and on the same 168-clip sweep: ASR word error rate 0.062 for both, and a MOS-predictor score 0.12 higher. - Quantized bundles compared with full precision through the runtime on lines covering every voice, each control at its limits and each language. The WebGPU bundle reproduces the full-precision take (identical frame counts on 15 of 17 lines, 0.7 dB median mel-cepstral distance). Dynamic INT8 moves some phone durations by a frame, which redraws a passage's noise and gives a different take, so the ONNX and LiteRT bundles are judged on the same sweep: ASR word error rate 0.063 (ONNX) and 0.062 (LiteRT) against 0.062 for full precision, and a MOS-predictor score within 0.02 of full precision for both. - Every LiteRT graph gives identical output with and without XNNPACK's weight cache. +- Core ML and Core AI bundles compared with the PyTorch model on every graph and through the runtime on 14 passages (every voice, each control at both limits, six languages, narration), on each compute unit they are used with (GPU, CPU, and for Core AI the Neural Engine): identical phonemes, fewer changed phone durations than the LiteRT bundle (0.2–0.7 % against 1.9 %), and smaller pitch-contour (0.016–0.045 semitones RMS against 0.051), latent and spectral differences. The Apple exports rewrite three computations into equivalent forms that FP16 and the Neural Engine evaluate exactly (span pooling, the modifier bit decoding, the duration model's combined phone/segment index), and scale the residual stream of the flow and the pitch predictor; in FP32 the rewritten graphs match the trained ones (identical outputs; the span encoders within 1e-6). Listening is decisive; automatic scores support it. 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0000000000000000000000000000000000000000..25000cd87fa6af2f2a10742de52a271c955ada92 --- /dev/null +++ b/coreai/assets/g2p/export_config.json @@ -0,0 +1,33 @@ +{ + "batch_dynamic": true, + "format": "scyllasband_g2p", + "input_granularity": "phrase", + "languages": [ + "de", + "en_gb", + "en_us", + "es", + "fr", + "it", + "vi" + ], + "model_type": "transformer", + "onnx_export_batch_size": 4, + "onnx_export_layout": "batch_first", + "onnx_export_max_batch_size": 256, + "onnx_exporter": "dynamo", + "onnx_fp32_model": "model.onnx", + "onnx_model": "model.onnx", + "onnx_padded_output": "ctc_blank", + "onnx_quantization": "none", + "onnx_quantized_parity_tolerance": 3.0, + "opset": 18, + "phoneme_token_mode": "char", + "phrase_rows_per_language": 200000, + "preserve_commas": true, + "punctuation_silence_target": "explicit_silence", + "release_role": "fp32_bundle_frontend", + "source_checkpoint": "output/deepphonemizer_multilingual/model_v10_7lang/checkpoints/model_step_250k.pt", + "training_mix": "phrase_word_chunked_clean", + "word_rows_per_language": 200000 +} diff --git a/coreai/assets/g2p/language_map.json b/coreai/assets/g2p/language_map.json new file mode 100644 index 0000000000000000000000000000000000000000..e70f9bff55415d972fbc5de94b878686d47a88c3 --- /dev/null +++ b/coreai/assets/g2p/language_map.json @@ -0,0 +1,47 @@ +{ + "american": "en_us", + "american_english": "en_us", + "british": "en_gb", + "british_english": "en_gb", + "de": "de", + "de_de": "de", + "deu": "de", + "en": "en_us", + "en-gb": "en_gb", + "en-uk": "en_gb", + "en-us": "en_us", + "en_gb": "en_gb", + "en_uk": "en_gb", + "en_us": "en_us", + "eng": "en_us", + "eng_gb": "en_gb", + "eng_us": "en_us", + "english": "en_us", + "english_gb": "en_gb", + "english_uk": "en_gb", + "english_us": "en_us", + "es": "es", + "es_es": "es", + "es_mx": "es", + "fr": "fr", + "fr_fr": "fr", + "fra": "fr", + "fre": "fr", + "french": "fr", + "gb": "en_gb", + "ger": "de", + "german": "de", + "it": "it", + 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0000000000000000000000000000000000000000..25000cd87fa6af2f2a10742de52a271c955ada92 --- /dev/null +++ b/coreml/assets/g2p/export_config.json @@ -0,0 +1,33 @@ +{ + "batch_dynamic": true, + "format": "scyllasband_g2p", + "input_granularity": "phrase", + "languages": [ + "de", + "en_gb", + "en_us", + "es", + "fr", + "it", + "vi" + ], + "model_type": "transformer", + "onnx_export_batch_size": 4, + "onnx_export_layout": "batch_first", + "onnx_export_max_batch_size": 256, + "onnx_exporter": "dynamo", + "onnx_fp32_model": "model.onnx", + "onnx_model": "model.onnx", + "onnx_padded_output": "ctc_blank", + "onnx_quantization": "none", + "onnx_quantized_parity_tolerance": 3.0, + "opset": 18, + "phoneme_token_mode": "char", + "phrase_rows_per_language": 200000, + "preserve_commas": true, + "punctuation_silence_target": "explicit_silence", + "release_role": "fp32_bundle_frontend", + "source_checkpoint": "output/deepphonemizer_multilingual/model_v10_7lang/checkpoints/model_step_250k.pt", + "training_mix": "phrase_word_chunked_clean", + "word_rows_per_language": 200000 +} diff --git a/coreml/assets/g2p/language_map.json b/coreml/assets/g2p/language_map.json new file mode 100644 index 0000000000000000000000000000000000000000..e70f9bff55415d972fbc5de94b878686d47a88c3 --- /dev/null +++ b/coreml/assets/g2p/language_map.json @@ -0,0 +1,47 @@ +{ + "american": "en_us", + "american_english": "en_us", + "british": "en_gb", + "british_english": "en_gb", + "de": "de", + "de_de": "de", + "deu": "de", + "en": "en_us", + "en-gb": "en_gb", + "en-uk": "en_gb", + "en-us": "en_us", + "en_gb": "en_gb", + "en_uk": "en_gb", + "en_us": "en_us", + "eng": "en_us", + "eng_gb": "en_gb", + "eng_us": "en_us", + "english": "en_us", + "english_gb": "en_gb", + "english_uk": "en_gb", + "english_us": "en_us", + "es": "es", + "es_es": "es", + "es_mx": "es", + "fr": "fr", + "fr_fr": "fr", + "fra": "fr", + "fre": "fr", + "french": "fr", + "gb": "en_gb", + "ger": "de", + "german": "de", + "it": "it", + 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b/coreml/assets/g2p/pronunciation_overrides.json new file mode 100644 index 0000000000000000000000000000000000000000..46ee8e9d368a4a75545f4d8a76213ce4209dddf3 --- /dev/null +++ b/coreml/assets/g2p/pronunciation_overrides.json @@ -0,0 +1,4 @@ +{ + "format": "scyllasband_g2p_pronunciation_overrides_v1", + "languages": {} +} diff --git a/coreml/assets/g2p/tokenizer.json b/coreml/assets/g2p/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..cc6a708c009b634d46c8cec0aa9286bfb38e7fa4 --- /dev/null +++ b/coreml/assets/g2p/tokenizer.json @@ -0,0 +1,517 @@ +{ + "format": "deepphonemizer_forward_ctc", + "languages": [ + "de", + "en_gb", + "en_us", + "es", + "fr", + "it", + "vi" + ], + "char_repeats": 4, + "lowercase": true, + "phoneme_token_mode": "char", + "text_symbols": { + "_": 0, + "": 1, + "": 2, + "": 3, + "": 4, + "": 5, + "": 6, + "": 7, + "": 8, + "\b": 9, + " ": 10, + "!": 11, + "\"": 12, + "#": 13, + "$": 14, + "%": 15, + "&": 16, + "'": 17, + "(": 18, 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+ "₁": 359, + "₂": 360, + "₃": 361, + "₄": 362, + "₅": 363, + "₆": 364, + "₇": 365, + "₈": 366, + "€": 367, + "₹": 368, + "⅓": 369, + "⅔": 370, + "⅖": 371, + "⅙": 372, + "⅛": 373, + "⅝": 374, + "√": 375, + "│": 376, + "♦": 377, + "⚓": 378, + "✈": 379, + "✠": 380, + "✨": 381, + "⟙": 382, + "⸺": 383, + "。": 384, + "〈": 385, + "な": 386, + "三": 387, + "次": 388, + "肿": 389, + "臃": 390, + "구": 391, + "로": 392, + "요": 393, + "ﷺ": 394, + "️": 395, + "𝑥": 396, + "𝑦": 397, + "🌊": 398, + "🌿": 399, + "💦": 400, + "💨": 401 + }, + "phoneme_symbols": { + "0": "_", + "1": "", + "2": "", + "3": "", + "4": "", + "5": "", + "6": "", + "7": "", + "8": "", + "9": " ", + "10": "!", + "11": ",", + "12": "-", + "13": ".", + "14": "1", + "15": "2", + "16": "4", + "17": "5", + "18": "6", + "19": "7", + "20": ":", + "21": ";", + "22": "?", + "23": "a", + "24": "b", + "25": "c", + "26": "d", + "27": "e", + "28": "f", + "29": "h", + "30": "i", + "31": "j", + "32": "k", + "33": "l", + "34": "m", + "35": "n", + "36": "o", + "37": "p", + "38": "q", + "39": "r", + "40": "s", + "41": "t", + "42": "u", + "43": "v", + "44": "w", + "45": "x", + "46": "y", + "47": "z", + "48": "«", + "49": "»", + "50": "æ", + "51": "ç", + "52": "ð", + "53": "ø", + "54": "ŋ", + "55": "œ", + "56": "ɐ", + "57": "ɑ", + "58": "ɒ", + "59": "ɔ", + "60": "ɗ", + "61": "ə", + "62": "ɚ", + "63": "ɛ", + "64": "ɜ", + "65": "ɟ", + "66": "ɡ", + "67": "ɣ", + "68": "ɨ", + "69": "ɪ", + "70": "ɬ", + "71": "ɯ", + "72": "ɲ", + "73": "ɹ", + "74": "ɾ", + "75": "ʁ", + "76": "ʃ", + "77": "ʊ", + "78": "ʌ", + "79": "ʎ", + "80": "ʐ", + "81": "ʒ", + "82": "ʔ", + "83": "ʝ", + "84": "ʲ", + "85": "ˈ", + "86": "ˌ", + "87": "ː", + "88": "̃", + "89": "̩", + "90": "̪", + "91": "β", + "92": "θ", + "93": "ᵻ", + "94": "—", + "95": "…" + } +} diff --git a/coreml/assets/languages.json b/coreml/assets/languages.json new file mode 100644 index 0000000000000000000000000000000000000000..733e6d6703aa1635deb1d242db8c48e2e3743059 --- /dev/null +++ b/coreml/assets/languages.json @@ -0,0 +1,30 @@ +[ + { + "id": "en_us", + "index": 0 + }, + { + "id": "en_gb", + "index": 1 + }, + { + "id": "es", + "index": 2 + }, + { + "id": "it", + "index": 3 + }, + { + "id": "fr", + "index": 4 + }, + { + "id": "de", + "index": 5 + }, + { + "id": "vi", + "index": 6 + } +] diff --git a/coreml/assets/phone_vocab.json b/coreml/assets/phone_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..4e3fc8dfaa63902e040737865f3d5a4edd5320d8 --- /dev/null +++ b/coreml/assets/phone_vocab.json @@ -0,0 +1,111 @@ +{ + "schema_version": 1, + "mode": "scyllasband_ipa_phone_symbols", + "special_tokens": [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "vocab_size": 87, + "token_to_id": { + "": 0, + "": 1, + "": 2, + "": 3, + "": 4, + "": 5, + "": 6, + "": 7, + "": 8, + "": 9, + "": 10, + "": 11, + "": 12, + "": 13, + "": 14, + "1": 15, + "2": 16, + "4": 17, + "5": 18, + "6": 19, + "7": 20, + "a": 21, + "b": 22, + "c": 23, + "d": 24, + "e": 25, + "f": 26, + "h": 27, + "i": 28, + "j": 29, + "k": 30, + "l": 31, + "m": 32, + "n": 33, + "o": 34, + "p": 35, + "r": 36, + "s": 37, + "t": 38, + "u": 39, + "v": 40, + "w": 41, + "x": 42, + "y": 43, + "z": 44, + "æ": 45, + "ç": 46, + "ð": 47, + "ø": 48, + "ŋ": 49, + "œ": 50, + "ɐ": 51, + "ɑ": 52, + "ɒ": 53, + "ɔ": 54, + "ɗ": 55, + "ə": 56, + "ɚ": 57, + "ɛ": 58, + "ɜ": 59, + "ɟ": 60, + "ɡ": 61, + "ɣ": 62, + "ɪ": 63, + "ɯ": 64, + "ɲ": 65, + "ɹ": 66, + "ɾ": 67, + "ʁ": 68, + "ʃ": 69, + "ʊ": 70, + "ʌ": 71, + "ʎ": 72, + "ʐ": 73, + "ʒ": 74, + "ʔ": 75, + "ʝ": 76, + "ʲ": 77, + "ˈ": 78, + "ˌ": 79, + "ː": 80, + "̃": 81, + "̩": 82, + "̪": 83, + "β": 84, + "θ": 85, + "ᵻ": 86 + } +} diff --git a/coreml/assets/voices.json b/coreml/assets/voices.json new file mode 100644 index 0000000000000000000000000000000000000000..f62fb6a714623d367f045bd8452a079b2340fd19 --- /dev/null +++ b/coreml/assets/voices.json @@ -0,0 +1,58 @@ +[ + { + "id": "ariadne", + "index": 0 + }, + { + "id": "felix", + "index": 1 + }, + { + "id": "gwen", + "index": 2 + }, + { + "id": "ink", + "index": 3 + }, + { + "id": "max", + "index": 4 + }, + { + "id": "orpheus", + "index": 5 + }, + { + "id": "rex", + "index": 6 + }, + { + "id": "scylla", + "index": 7 + }, + { + "id": "stone", + "index": 8 + }, + { + "id": "tuesday", + "index": 9 + }, + { + "id": "thistle", + "index": 10 + }, + { + "id": "kincade", + "index": 11 + }, + { + "id": "poppy", + "index": 12 + }, + { + "id": "priya", + "index": 13 + } +] diff --git a/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin b/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..d5e398b08d2466e30271a65e21d2ccb05e17bd5f --- /dev/null +++ b/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff481a2cd37fa03e16ecda507b033ec1f76b69469819a3259e3574bf9ebe72ab +size 243 diff --git a/coreml/coreml/duration_predictor.mlmodelc/coremldata.bin b/coreml/coreml/duration_predictor.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cf7962c1d70c21ef10e49ea12898bde2bd912866 --- /dev/null +++ b/coreml/coreml/duration_predictor.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06bfdf64690cc0de4e97a0af486c678bf498b4b4224159fd40af1ba8ae296c60 +size 554 diff --git a/coreml/coreml/duration_predictor.mlmodelc/model.mil b/coreml/coreml/duration_predictor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b0cb4856856184689eb441f96ed3eecdd0adab65 --- /dev/null +++ b/coreml/coreml/duration_predictor.mlmodelc/model.mil @@ -0,0 +1,776 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor delivery_present, tensor delivery_values, tensor language_id, tensor span_mask, tensor span_phone_ids, tensor span_segment_ids, tensor voice_id) { + int32 var_19 = const()[name = string("op_19"), val = int32(0)]; + tensor span_mask_1 = not_equal(x = span_mask, y = var_19)[name = string("span_mask_1")]; + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_52 = const()[name = string("op_52"), val = int32(-1)]; + int32 var_94 = const()[name = string("op_94"), val = int32(7)]; + tensor var_95 = mul(x = voice_id, y = var_94)[name = string("op_95")]; + tensor index = add(x = var_95, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_121_dtype_0 = const()[name = string("cast_121_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_11")]; + tensor cast_121 = cast(dtype = cast_121_dtype_0, x = index_to_int16)[name = string("cast_10")]; + tensor greater_equal_0 = greater_equal(x = cast_121, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_16 = const()[name = string("slice_by_index_16"), val = int32(98)]; + tensor add_0 = add(x = cast_121, y = slice_by_index_16)[name = string("add_0")]; + tensor select_0 = select(a = cast_121, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_9")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_8")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_7")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + int32 var_121 = const()[name = string("op_121"), val = int32(87)]; + tensor var_122 = mul(x = span_segment_ids, y = var_121)[name = string("op_122")]; + tensor input_1 = add(x = var_122, y = span_phone_ids)[name = string("input_1")]; + int32 var_129_axis_0 = const()[name = string("op_129_axis_0"), val = int32(0)]; + int32 var_129_batch_dims_0 = const()[name = string("op_129_batch_dims_0"), val = int32(0)]; + bool var_129_validate_indices_0 = const()[name = string("op_129_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153536))))[name = string("module_model_phone_embed_table_weight_to_fp16_quantized")]; + string input_1_to_uint16_dtype_0 = const()[name = string("input_1_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_1_to_uint16 = cast(dtype = input_1_to_uint16_dtype_0, x = input_1)[name = string("cast_6")]; + tensor var_129_cast_fp16_cast_uint16 = gather(axis = var_129_axis_0, batch_dims = var_129_batch_dims_0, indices = input_1_to_uint16, validate_indices = var_129_validate_indices_0, x = module_model_phone_embed_table_weight_to_fp16_quantized)[name = string("op_129_cast_fp16_cast_uint16")]; + tensor op_131_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154176))), scale = tensor([[[0x1.4d4p-5]]]))[name = string("op_131_to_fp16_quantized")]; + tensor hidden_1_cast_fp16 = add(x = var_129_cast_fp16_cast_uint16, y = op_131_to_fp16_quantized)[name = string("hidden_1_cast_fp16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_136_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_136_cast_fp16")]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + tensor input_5_cast_fp16 = concat(axis = var_52, interleave = input_5_interleave_0, values = (var_136_cast_fp16, present_to_fp16))[name = string("input_5_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318080)))]; + tensor linear_0_bias_0_to_fp16 = const()[name = string("linear_0_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324544)))]; + tensor linear_0_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_5_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_141_axes_0 = const()[name = string("op_141_axes_0"), val = tensor([1])]; + tensor var_141_cast_fp16 = expand_dims(axes = var_141_axes_0, x = linear_0_cast_fp16)[name = string("op_141_cast_fp16")]; + tensor hidden_3_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_141_cast_fp16)[name = string("hidden_3_cast_fp16")]; + int32 var_145_axis_0 = const()[name = string("op_145_axis_0"), val = int32(0)]; + int32 var_145_batch_dims_0 = const()[name = string("op_145_batch_dims_0"), val = int32(0)]; + bool var_145_validate_indices_0 = const()[name = string("op_145_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329792))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_3")]; + tensor var_145_cast_fp16_cast_uint16 = gather(axis = var_145_axis_0, batch_dims = var_145_batch_dims_0, indices = voice_id_to_uint16, validate_indices = var_145_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("op_145_cast_fp16_cast_uint16")]; + int32 var_147_axis_0 = const()[name = string("op_147_axis_0"), val = int32(0)]; + int32 var_147_batch_dims_0 = const()[name = string("op_147_batch_dims_0"), val = int32(0)]; + bool var_147_validate_indices_0 = const()[name = string("op_147_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329920)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_2")]; + tensor var_147_cast_fp16_cast_uint16 = gather(axis = var_147_axis_0, batch_dims = var_147_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_147_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("op_147_cast_fp16_cast_uint16")]; + tensor var_148_cast_fp16 = add(x = var_145_cast_fp16_cast_uint16, y = var_147_cast_fp16_cast_uint16)[name = string("op_148_cast_fp16")]; + tensor var_150_to_fp16 = const()[name = string("op_150_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334464)))]; + tensor var_151_cast_fp16 = add(x = var_148_cast_fp16, y = var_150_to_fp16)[name = string("op_151_cast_fp16")]; + tensor var_154_cast_fp16 = add(x = var_151_cast_fp16, y = var_150_to_fp16)[name = string("op_154_cast_fp16")]; + tensor var_155_axes_0 = const()[name = string("op_155_axes_0"), val = tensor([1])]; + tensor var_155_cast_fp16 = expand_dims(axes = var_155_axes_0, x = var_154_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_5_cast_fp16 = add(x = hidden_3_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_5_cast_fp16")]; + fp16 var_51_to_fp16 = const()[name = string("op_51_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_50_to_fp16 = const()[name = string("op_50_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_51_to_fp16, beta = var_50_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor baseline_duration_mask_internal_tensor_assign_1_to_fp16 = const()[name = string("baseline_duration_mask_internal_tensor_assign_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335168)))]; + tensor baseline_mask_cast_fp16 = mul(x = clip_0_cast_fp16, y = baseline_duration_mask_internal_tensor_assign_1_to_fp16)[name = string("baseline_mask_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_51_to_fp16, beta = var_50_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_179_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = baseline_mask_cast_fp16)[name = string("op_179_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345600))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346304)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_179_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_185_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = baseline_mask_cast_fp16)[name = string("op_185_cast_fp16")]; + tensor var_186_axes_0 = const()[name = string("op_186_axes_0"), val = tensor([-1])]; + tensor var_186_cast_fp16 = expand_dims(axes = var_186_axes_0, x = clip_1_cast_fp16)[name = string("op_186_cast_fp16")]; + tensor var_187_cast_fp16 = mul(x = var_185_cast_fp16, y = var_186_cast_fp16)[name = string("op_187_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357312))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_187_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor conditioned_cast_fp16 = add(x = linear_1_cast_fp16, y = linear_2_cast_fp16)[name = string("conditioned_cast_fp16")]; + tensor var_194_axes_0 = const()[name = string("op_194_axes_0"), val = tensor([1])]; + tensor var_194_cast_fp16 = expand_dims(axes = var_194_axes_0, x = conditioned_cast_fp16)[name = string("op_194_cast_fp16")]; + tensor hidden_cast_fp16 = add(x = hidden_5_cast_fp16, y = var_194_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_204_axes_0 = const()[name = string("op_204_axes_0"), val = tensor([-1])]; + tensor var_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = mask_7_cast_fp16)[name = string("op_204_cast_fp16")]; + tensor input_13_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_204_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521920))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_3_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_13_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_209_axes_0 = const()[name = string("op_209_axes_0"), val = tensor([1])]; + tensor var_209_cast_fp16 = expand_dims(axes = var_209_axes_0, x = linear_3_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor src_cast_fp16 = add(x = hidden_cast_fp16, y = var_209_cast_fp16)[name = string("src_cast_fp16")]; + tensor mask = logical_not(x = span_mask_1)[name = string("mask")]; + fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(-inf)]; + tensor var_229_to_fp16 = const()[name = string("op_229_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522624)))]; + tensor key_padding_mask_1_cast_fp16 = select(a = var_35_to_fp16, b = var_229_to_fp16, cond = mask)[name = string("key_padding_mask_1_cast_fp16")]; + tensor query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_0_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(523712)))]; + tensor module_model_encoder_layers_0_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416)))]; + fp16 var_45_to_fp16 = const()[name = string("op_45_to_fp16"), val = fp16(0x1.5p-17)]; + tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_model_encoder_layers_0_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_0_norm1_weight_to_fp16, x = src_cast_fp16)[name = string("query_1_cast_fp16")]; + tensor query_3_perm_0 = const()[name = string("query_3_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(832384))))[name = string("module_model_encoder_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(834368)))]; + tensor query_3_cast_fp16 = transpose(perm = query_3_perm_0, x = query_1_cast_fp16)[name = string("transpose_55")]; + tensor linear_4_cast_fp16 = linear(bias = module_model_encoder_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor concat_4 = const()[name = string("concat_4"), val = tensor([512, 1, 3, 320])]; + tensor var_263_cast_fp16 = reshape(shape = concat_4, x = linear_4_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor var_264_axes_0 = const()[name = string("op_264_axes_0"), val = tensor([0])]; + tensor var_264_cast_fp16 = expand_dims(axes = var_264_axes_0, x = var_263_cast_fp16)[name = string("op_264_cast_fp16")]; + tensor var_265_perm_0 = const()[name = string("op_265_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_266_axes_0 = const()[name = string("op_266_axes_0"), val = tensor([-2])]; + tensor var_265_cast_fp16 = transpose(perm = var_265_perm_0, x = var_264_cast_fp16)[name = string("transpose_54")]; + tensor var_266_cast_fp16 = squeeze(axes = var_266_axes_0, x = var_265_cast_fp16)[name = string("op_266_cast_fp16")]; + tensor q_1_begin_0 = const()[name = string("q_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_1_end_0 = const()[name = string("q_1_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_1_end_mask_0 = const()[name = string("q_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_1_squeeze_mask_0 = const()[name = string("q_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_1_cast_fp16 = slice_by_index(begin = q_1_begin_0, end = q_1_end_0, end_mask = q_1_end_mask_0, squeeze_mask = q_1_squeeze_mask_0, x = var_266_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor k_1_begin_0 = const()[name = string("k_1_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_1_end_0 = const()[name = string("k_1_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_1_end_mask_0 = const()[name = string("k_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_1_squeeze_mask_0 = const()[name = string("k_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_1_cast_fp16 = slice_by_index(begin = k_1_begin_0, end = k_1_end_0, end_mask = k_1_end_mask_0, squeeze_mask = k_1_squeeze_mask_0, x = var_266_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor v_1_begin_0 = const()[name = string("v_1_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_1_end_0 = const()[name = string("v_1_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_1_end_mask_0 = const()[name = string("v_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_1_squeeze_mask_0 = const()[name = string("v_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_1_cast_fp16 = slice_by_index(begin = v_1_begin_0, end = v_1_end_0, end_mask = v_1_end_mask_0, squeeze_mask = v_1_squeeze_mask_0, x = var_266_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor var_274 = const()[name = string("op_274"), val = tensor([512, 8, 40])]; + tensor var_275_cast_fp16 = reshape(shape = var_274, x = q_1_cast_fp16)[name = string("op_275_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([1, 0, 2])]; + tensor var_281 = const()[name = string("op_281"), val = tensor([512, 8, 40])]; + tensor var_282_cast_fp16 = reshape(shape = var_281, x = k_1_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor k_3_perm_0 = const()[name = string("k_3_perm_0"), val = tensor([1, 0, 2])]; + tensor var_288 = const()[name = string("op_288"), val = tensor([512, 8, 40])]; + tensor var_289_cast_fp16 = reshape(shape = var_288, x = v_1_cast_fp16)[name = string("op_289_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([1, 0, 2])]; + tensor var_292 = const()[name = string("op_292"), val = tensor([1, 1, 1, 512])]; + tensor var_293_cast_fp16 = reshape(shape = var_292, x = key_padding_mask_1_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor var_295_reps_0 = const()[name = string("op_295_reps_0"), val = tensor([1, 8, 1, 1])]; + tensor var_295_cast_fp16 = tile(reps = var_295_reps_0, x = var_293_cast_fp16)[name = string("op_295_cast_fp16")]; + tensor var_303 = const()[name = string("op_303"), val = tensor([1, 8, 512, 40])]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_275_cast_fp16)[name = string("transpose_53")]; + tensor q_5_cast_fp16 = reshape(shape = var_303, x = q_3_cast_fp16)[name = string("q_5_cast_fp16")]; + tensor var_305 = const()[name = string("op_305"), val = tensor([1, 8, 512, 40])]; + tensor k_3_cast_fp16 = transpose(perm = k_3_perm_0, x = var_282_cast_fp16)[name = string("transpose_52")]; + tensor k_5_cast_fp16 = reshape(shape = var_305, x = k_3_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor var_307 = const()[name = string("op_307"), val = tensor([1, 8, 512, 40])]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_289_cast_fp16)[name = string("transpose_51")]; + tensor v_5_cast_fp16 = reshape(shape = var_307, x = v_3_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor attn_output_1_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_5_cast_fp16, query = q_5_cast_fp16, value = v_5_cast_fp16)[name = string("attn_output_1_cast_fp16")]; + tensor var_310 = const()[name = string("op_310"), val = tensor([2, 0, 1, 3])]; + tensor var_315 = const()[name = string("op_315"), val = tensor([512, 320])]; + tensor var_311_cast_fp16 = transpose(perm = var_310, x = attn_output_1_cast_fp16)[name = string("transpose_50")]; + tensor attn_output_3_cast_fp16 = reshape(shape = var_315, x = var_311_cast_fp16)[name = string("attn_output_3_cast_fp16")]; + tensor module_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(938816))))[name = string("module_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939520)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_3_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_319 = const()[name = string("op_319"), val = tensor([512, 1, 320])]; + tensor attn_output_7_cast_fp16 = reshape(shape = var_319, x = linear_5_cast_fp16)[name = string("attn_output_7_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([1, 0, 2])]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = attn_output_7_cast_fp16)[name = string("transpose_49")]; + tensor input_17_cast_fp16 = add(x = src_cast_fp16, y = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor input_19_axes_0 = const()[name = string("input_19_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_0_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940224)))]; + tensor module_model_encoder_layers_0_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940928)))]; + tensor input_19_cast_fp16 = layer_norm(axes = input_19_axes_0, beta = module_model_encoder_layers_0_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_0_norm2_weight_to_fp16, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_model_encoder_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1351296))))[name = string("module_model_encoder_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_0_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353920)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_encoder_layers_0_linear1_bias_to_fp16, weight = module_model_encoder_layers_0_linear1_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_6_cast_fp16")]; + string input_21_mode_0 = const()[name = string("input_21_mode_0"), val = string("EXACT")]; + tensor input_21_cast_fp16 = gelu(mode = input_21_mode_0, x = linear_6_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor module_model_encoder_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1356544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1766208))))[name = string("module_model_encoder_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_0_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1766912)))]; + tensor linear_7_cast_fp16 = linear(bias = module_model_encoder_layers_0_linear2_bias_to_fp16, weight = module_model_encoder_layers_0_linear2_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_27_cast_fp16 = add(x = input_17_cast_fp16, y = linear_7_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_1_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1767616)))]; + tensor module_model_encoder_layers_1_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1768320)))]; + tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_model_encoder_layers_1_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_1_norm1_weight_to_fp16, x = input_27_cast_fp16)[name = string("query_5_cast_fp16")]; + tensor query_7_perm_0 = const()[name = string("query_7_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1769024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2076288))))[name = string("module_model_encoder_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2078272)))]; + tensor query_7_cast_fp16 = transpose(perm = query_7_perm_0, x = query_5_cast_fp16)[name = string("transpose_48")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_encoder_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor concat_6 = const()[name = string("concat_6"), val = tensor([512, 1, 3, 320])]; + tensor var_370_cast_fp16 = reshape(shape = concat_6, x = linear_8_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_axes_0 = const()[name = string("op_371_axes_0"), val = tensor([0])]; + tensor var_371_cast_fp16 = expand_dims(axes = var_371_axes_0, x = var_370_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_372_perm_0 = const()[name = string("op_372_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-2])]; + tensor var_372_cast_fp16 = transpose(perm = var_372_perm_0, x = var_371_cast_fp16)[name = string("transpose_47")]; + tensor var_373_cast_fp16 = squeeze(axes = var_373_axes_0, x = var_372_cast_fp16)[name = string("op_373_cast_fp16")]; + tensor q_7_begin_0 = const()[name = string("q_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_7_end_0 = const()[name = string("q_7_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_7_end_mask_0 = const()[name = string("q_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_7_squeeze_mask_0 = const()[name = string("q_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_7_cast_fp16 = slice_by_index(begin = q_7_begin_0, end = q_7_end_0, end_mask = q_7_end_mask_0, squeeze_mask = q_7_squeeze_mask_0, x = var_373_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor k_7_begin_0 = const()[name = string("k_7_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_7_end_0 = const()[name = string("k_7_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_7_end_mask_0 = const()[name = string("k_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_7_squeeze_mask_0 = const()[name = string("k_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_7_cast_fp16 = slice_by_index(begin = k_7_begin_0, end = k_7_end_0, end_mask = k_7_end_mask_0, squeeze_mask = k_7_squeeze_mask_0, x = var_373_cast_fp16)[name = string("k_7_cast_fp16")]; + tensor v_7_begin_0 = const()[name = string("v_7_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_7_end_0 = const()[name = string("v_7_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_7_end_mask_0 = const()[name = string("v_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_7_squeeze_mask_0 = const()[name = string("v_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_7_cast_fp16 = slice_by_index(begin = v_7_begin_0, end = v_7_end_0, end_mask = v_7_end_mask_0, squeeze_mask = v_7_squeeze_mask_0, x = var_373_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor var_381 = const()[name = string("op_381"), val = tensor([512, 8, 40])]; + tensor var_382_cast_fp16 = reshape(shape = var_381, x = q_7_cast_fp16)[name = string("op_382_cast_fp16")]; + tensor q_9_perm_0 = const()[name = string("q_9_perm_0"), val = tensor([1, 0, 2])]; + tensor var_388 = const()[name = string("op_388"), val = tensor([512, 8, 40])]; + tensor var_389_cast_fp16 = reshape(shape = var_388, x = k_7_cast_fp16)[name = string("op_389_cast_fp16")]; + tensor k_9_perm_0 = const()[name = string("k_9_perm_0"), val = tensor([1, 0, 2])]; + tensor var_395 = const()[name = string("op_395"), val = tensor([512, 8, 40])]; + tensor var_396_cast_fp16 = reshape(shape = var_395, x = v_7_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([1, 0, 2])]; + tensor var_410 = const()[name = string("op_410"), val = tensor([1, 8, 512, 40])]; + tensor q_9_cast_fp16 = transpose(perm = q_9_perm_0, x = var_382_cast_fp16)[name = string("transpose_46")]; + tensor q_11_cast_fp16 = reshape(shape = var_410, x = q_9_cast_fp16)[name = string("q_11_cast_fp16")]; + tensor var_412 = const()[name = string("op_412"), val = tensor([1, 8, 512, 40])]; + tensor k_9_cast_fp16 = transpose(perm = k_9_perm_0, x = var_389_cast_fp16)[name = string("transpose_45")]; + tensor k_11_cast_fp16 = reshape(shape = var_412, x = k_9_cast_fp16)[name = string("k_11_cast_fp16")]; + tensor var_414 = const()[name = string("op_414"), val = tensor([1, 8, 512, 40])]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_396_cast_fp16)[name = string("transpose_44")]; + tensor v_11_cast_fp16 = reshape(shape = var_414, x = v_9_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor attn_output_9_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_11_cast_fp16, query = q_11_cast_fp16, value = v_11_cast_fp16)[name = string("attn_output_9_cast_fp16")]; + tensor var_417 = const()[name = string("op_417"), val = tensor([2, 0, 1, 3])]; + tensor var_422 = const()[name = string("op_422"), val = tensor([512, 320])]; + tensor var_418_cast_fp16 = transpose(perm = var_417, x = attn_output_9_cast_fp16)[name = string("transpose_43")]; + tensor attn_output_11_cast_fp16 = reshape(shape = var_422, x = var_418_cast_fp16)[name = string("attn_output_11_cast_fp16")]; + tensor module_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2080256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2182720))))[name = string("module_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2183424)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_426 = const()[name = string("op_426"), val = tensor([512, 1, 320])]; + tensor attn_output_15_cast_fp16 = reshape(shape = var_426, x = linear_9_cast_fp16)[name = string("attn_output_15_cast_fp16")]; + tensor input_29_perm_0 = const()[name = string("input_29_perm_0"), val = tensor([1, 0, 2])]; + tensor input_29_cast_fp16 = transpose(perm = input_29_perm_0, x = attn_output_15_cast_fp16)[name = string("transpose_42")]; + tensor input_31_cast_fp16 = add(x = input_27_cast_fp16, y = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor input_33_axes_0 = const()[name = string("input_33_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_1_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2184128)))]; + tensor module_model_encoder_layers_1_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2184832)))]; + tensor input_33_cast_fp16 = layer_norm(axes = input_33_axes_0, beta = module_model_encoder_layers_1_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_1_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_encoder_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2185536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2595200))))[name = string("module_model_encoder_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_1_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2597824)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_layers_1_linear1_bias_to_fp16, weight = module_model_encoder_layers_1_linear1_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_10_cast_fp16")]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("EXACT")]; + tensor input_35_cast_fp16 = gelu(mode = input_35_mode_0, x = linear_10_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2600448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3010112))))[name = string("module_model_encoder_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_1_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3010816)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_layers_1_linear2_bias_to_fp16, weight = module_model_encoder_layers_1_linear2_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_31_cast_fp16, y = linear_11_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_2_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3011520)))]; + tensor module_model_encoder_layers_2_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3012224)))]; + tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_model_encoder_layers_2_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_2_norm1_weight_to_fp16, x = input_41_cast_fp16)[name = string("query_9_cast_fp16")]; + tensor query_11_perm_0 = const()[name = string("query_11_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3012928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3320192))))[name = string("module_model_encoder_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3322176)))]; + tensor query_11_cast_fp16 = transpose(perm = query_11_perm_0, x = query_9_cast_fp16)[name = string("transpose_41")]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor concat_8 = const()[name = string("concat_8"), val = tensor([512, 1, 3, 320])]; + tensor var_477_cast_fp16 = reshape(shape = concat_8, x = linear_12_cast_fp16)[name = string("op_477_cast_fp16")]; + tensor var_478_axes_0 = const()[name = string("op_478_axes_0"), val = tensor([0])]; + tensor var_478_cast_fp16 = expand_dims(axes = var_478_axes_0, x = var_477_cast_fp16)[name = string("op_478_cast_fp16")]; + tensor var_479_perm_0 = const()[name = string("op_479_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_480_axes_0 = const()[name = string("op_480_axes_0"), val = tensor([-2])]; + tensor var_479_cast_fp16 = transpose(perm = var_479_perm_0, x = var_478_cast_fp16)[name = string("transpose_40")]; + tensor var_480_cast_fp16 = squeeze(axes = var_480_axes_0, x = var_479_cast_fp16)[name = string("op_480_cast_fp16")]; + tensor q_13_begin_0 = const()[name = string("q_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_13_end_0 = const()[name = string("q_13_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_13_end_mask_0 = const()[name = string("q_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_13_squeeze_mask_0 = const()[name = string("q_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_13_cast_fp16 = slice_by_index(begin = q_13_begin_0, end = q_13_end_0, end_mask = q_13_end_mask_0, squeeze_mask = q_13_squeeze_mask_0, x = var_480_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor k_13_begin_0 = const()[name = string("k_13_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_13_end_0 = const()[name = string("k_13_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_13_end_mask_0 = const()[name = string("k_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_13_squeeze_mask_0 = const()[name = string("k_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_13_cast_fp16 = slice_by_index(begin = k_13_begin_0, end = k_13_end_0, end_mask = k_13_end_mask_0, squeeze_mask = k_13_squeeze_mask_0, x = var_480_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor v_13_begin_0 = const()[name = string("v_13_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_13_end_0 = const()[name = string("v_13_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_13_end_mask_0 = const()[name = string("v_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_13_squeeze_mask_0 = const()[name = string("v_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_13_cast_fp16 = slice_by_index(begin = v_13_begin_0, end = v_13_end_0, end_mask = v_13_end_mask_0, squeeze_mask = v_13_squeeze_mask_0, x = var_480_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor var_488 = const()[name = string("op_488"), val = tensor([512, 8, 40])]; + tensor var_489_cast_fp16 = reshape(shape = var_488, x = q_13_cast_fp16)[name = string("op_489_cast_fp16")]; + tensor q_15_perm_0 = const()[name = string("q_15_perm_0"), val = tensor([1, 0, 2])]; + tensor var_495 = const()[name = string("op_495"), val = tensor([512, 8, 40])]; + tensor var_496_cast_fp16 = reshape(shape = var_495, x = k_13_cast_fp16)[name = string("op_496_cast_fp16")]; + tensor k_15_perm_0 = const()[name = string("k_15_perm_0"), val = tensor([1, 0, 2])]; + tensor var_502 = const()[name = string("op_502"), val = tensor([512, 8, 40])]; + tensor var_503_cast_fp16 = reshape(shape = var_502, x = v_13_cast_fp16)[name = string("op_503_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([1, 0, 2])]; + tensor var_517 = const()[name = string("op_517"), val = tensor([1, 8, 512, 40])]; + tensor q_15_cast_fp16 = transpose(perm = q_15_perm_0, x = var_489_cast_fp16)[name = string("transpose_39")]; + tensor q_17_cast_fp16 = reshape(shape = var_517, x = q_15_cast_fp16)[name = string("q_17_cast_fp16")]; + tensor var_519 = const()[name = string("op_519"), val = tensor([1, 8, 512, 40])]; + tensor k_15_cast_fp16 = transpose(perm = k_15_perm_0, x = var_496_cast_fp16)[name = string("transpose_38")]; + tensor k_17_cast_fp16 = reshape(shape = var_519, x = k_15_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor var_521 = const()[name = string("op_521"), val = tensor([1, 8, 512, 40])]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_503_cast_fp16)[name = string("transpose_37")]; + tensor v_17_cast_fp16 = reshape(shape = var_521, x = v_15_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor attn_output_17_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_17_cast_fp16, query = q_17_cast_fp16, value = v_17_cast_fp16)[name = string("attn_output_17_cast_fp16")]; + tensor var_524 = const()[name = string("op_524"), val = tensor([2, 0, 1, 3])]; + tensor var_529 = const()[name = string("op_529"), val = tensor([512, 320])]; + tensor var_525_cast_fp16 = transpose(perm = var_524, x = attn_output_17_cast_fp16)[name = string("transpose_36")]; + tensor attn_output_19_cast_fp16 = reshape(shape = var_529, x = var_525_cast_fp16)[name = string("attn_output_19_cast_fp16")]; + tensor module_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3324160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3426624))))[name = string("module_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3427328)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_19_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_533 = const()[name = string("op_533"), val = tensor([512, 1, 320])]; + tensor attn_output_23_cast_fp16 = reshape(shape = var_533, x = linear_13_cast_fp16)[name = string("attn_output_23_cast_fp16")]; + tensor input_43_perm_0 = const()[name = string("input_43_perm_0"), val = tensor([1, 0, 2])]; + tensor input_43_cast_fp16 = transpose(perm = input_43_perm_0, x = attn_output_23_cast_fp16)[name = string("transpose_35")]; + tensor input_45_cast_fp16 = add(x = input_41_cast_fp16, y = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_axes_0 = const()[name = string("input_47_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_2_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3428032)))]; + tensor module_model_encoder_layers_2_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3428736)))]; + tensor input_47_cast_fp16 = layer_norm(axes = input_47_axes_0, beta = module_model_encoder_layers_2_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_2_norm2_weight_to_fp16, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor module_model_encoder_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3429440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3839104))))[name = string("module_model_encoder_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_2_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3841728)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_layers_2_linear1_bias_to_fp16, weight = module_model_encoder_layers_2_linear1_weight_to_fp16_quantized, x = input_47_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_14_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4254016))))[name = string("module_model_encoder_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_2_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4254720)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_layers_2_linear2_bias_to_fp16, weight = module_model_encoder_layers_2_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_55_cast_fp16 = add(x = input_45_cast_fp16, y = linear_15_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_3_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4255424)))]; + tensor module_model_encoder_layers_3_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4256128)))]; + tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_model_encoder_layers_3_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_3_norm1_weight_to_fp16, x = input_55_cast_fp16)[name = string("query_13_cast_fp16")]; + tensor query_15_perm_0 = const()[name = string("query_15_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4256832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4564096))))[name = string("module_model_encoder_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4566080)))]; + tensor query_15_cast_fp16 = transpose(perm = query_15_perm_0, x = query_13_cast_fp16)[name = string("transpose_34")]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor concat_10 = const()[name = string("concat_10"), val = tensor([512, 1, 3, 320])]; + tensor var_584_cast_fp16 = reshape(shape = concat_10, x = linear_16_cast_fp16)[name = string("op_584_cast_fp16")]; + tensor var_585_axes_0 = const()[name = string("op_585_axes_0"), val = tensor([0])]; + tensor var_585_cast_fp16 = expand_dims(axes = var_585_axes_0, x = var_584_cast_fp16)[name = string("op_585_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([-2])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = var_585_cast_fp16)[name = string("transpose_33")]; + tensor var_587_cast_fp16 = squeeze(axes = var_587_axes_0, x = var_586_cast_fp16)[name = string("op_587_cast_fp16")]; + tensor q_19_begin_0 = const()[name = string("q_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_19_end_0 = const()[name = string("q_19_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_19_end_mask_0 = const()[name = string("q_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_19_squeeze_mask_0 = const()[name = string("q_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_19_cast_fp16 = slice_by_index(begin = q_19_begin_0, end = q_19_end_0, end_mask = q_19_end_mask_0, squeeze_mask = q_19_squeeze_mask_0, x = var_587_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor k_19_begin_0 = const()[name = string("k_19_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_19_end_0 = const()[name = string("k_19_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_19_end_mask_0 = const()[name = string("k_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_19_squeeze_mask_0 = const()[name = string("k_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_19_cast_fp16 = slice_by_index(begin = k_19_begin_0, end = k_19_end_0, end_mask = k_19_end_mask_0, squeeze_mask = k_19_squeeze_mask_0, x = var_587_cast_fp16)[name = string("k_19_cast_fp16")]; + tensor v_19_begin_0 = const()[name = string("v_19_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_19_end_0 = const()[name = string("v_19_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_19_end_mask_0 = const()[name = string("v_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_19_squeeze_mask_0 = const()[name = string("v_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_19_cast_fp16 = slice_by_index(begin = v_19_begin_0, end = v_19_end_0, end_mask = v_19_end_mask_0, squeeze_mask = v_19_squeeze_mask_0, x = var_587_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor var_595 = const()[name = string("op_595"), val = tensor([512, 8, 40])]; + tensor var_596_cast_fp16 = reshape(shape = var_595, x = q_19_cast_fp16)[name = string("op_596_cast_fp16")]; + tensor q_21_perm_0 = const()[name = string("q_21_perm_0"), val = tensor([1, 0, 2])]; + tensor var_602 = const()[name = string("op_602"), val = tensor([512, 8, 40])]; + tensor var_603_cast_fp16 = reshape(shape = var_602, x = k_19_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor k_21_perm_0 = const()[name = string("k_21_perm_0"), val = tensor([1, 0, 2])]; + tensor var_609 = const()[name = string("op_609"), val = tensor([512, 8, 40])]; + tensor var_610_cast_fp16 = reshape(shape = var_609, x = v_19_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor v_21_perm_0 = const()[name = string("v_21_perm_0"), val = tensor([1, 0, 2])]; + tensor var_624 = const()[name = string("op_624"), val = tensor([1, 8, 512, 40])]; + tensor q_21_cast_fp16 = transpose(perm = q_21_perm_0, x = var_596_cast_fp16)[name = string("transpose_32")]; + tensor q_23_cast_fp16 = reshape(shape = var_624, x = q_21_cast_fp16)[name = string("q_23_cast_fp16")]; + tensor var_626 = const()[name = string("op_626"), val = tensor([1, 8, 512, 40])]; + tensor k_21_cast_fp16 = transpose(perm = k_21_perm_0, x = var_603_cast_fp16)[name = string("transpose_31")]; + tensor k_23_cast_fp16 = reshape(shape = var_626, x = k_21_cast_fp16)[name = string("k_23_cast_fp16")]; + tensor var_628 = const()[name = string("op_628"), val = tensor([1, 8, 512, 40])]; + tensor v_21_cast_fp16 = transpose(perm = v_21_perm_0, x = var_610_cast_fp16)[name = string("transpose_30")]; + tensor v_23_cast_fp16 = reshape(shape = var_628, x = v_21_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor attn_output_25_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_23_cast_fp16, query = q_23_cast_fp16, value = v_23_cast_fp16)[name = string("attn_output_25_cast_fp16")]; + tensor var_631 = const()[name = string("op_631"), val = tensor([2, 0, 1, 3])]; + tensor var_636 = const()[name = string("op_636"), val = tensor([512, 320])]; + tensor var_632_cast_fp16 = transpose(perm = var_631, x = attn_output_25_cast_fp16)[name = string("transpose_29")]; + tensor attn_output_27_cast_fp16 = reshape(shape = var_636, x = var_632_cast_fp16)[name = string("attn_output_27_cast_fp16")]; + tensor module_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4568064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4670528))))[name = string("module_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4671232)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_27_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_640 = const()[name = string("op_640"), val = tensor([512, 1, 320])]; + tensor attn_output_31_cast_fp16 = reshape(shape = var_640, x = linear_17_cast_fp16)[name = string("attn_output_31_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([1, 0, 2])]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = attn_output_31_cast_fp16)[name = string("transpose_28")]; + tensor input_59_cast_fp16 = add(x = input_55_cast_fp16, y = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_axes_0 = const()[name = string("input_61_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_3_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4671936)))]; + tensor module_model_encoder_layers_3_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4672640)))]; + tensor input_61_cast_fp16 = layer_norm(axes = input_61_axes_0, beta = module_model_encoder_layers_3_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_3_norm2_weight_to_fp16, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor module_model_encoder_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4673344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5083008))))[name = string("module_model_encoder_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_3_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5085632)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_layers_3_linear1_bias_to_fp16, weight = module_model_encoder_layers_3_linear1_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("linear_18_cast_fp16")]; + string input_63_mode_0 = const()[name = string("input_63_mode_0"), val = string("EXACT")]; + tensor input_63_cast_fp16 = gelu(mode = input_63_mode_0, x = linear_18_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5088256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5497920))))[name = string("module_model_encoder_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_3_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5498624)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_layers_3_linear2_bias_to_fp16, weight = module_model_encoder_layers_3_linear2_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_59_cast_fp16, y = linear_19_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_4_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5499328)))]; + tensor module_model_encoder_layers_4_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5500032)))]; + tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_model_encoder_layers_4_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_4_norm1_weight_to_fp16, x = input_69_cast_fp16)[name = string("query_17_cast_fp16")]; + tensor query_19_perm_0 = const()[name = string("query_19_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5500736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5808000))))[name = string("module_model_encoder_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5809984)))]; + tensor query_19_cast_fp16 = transpose(perm = query_19_perm_0, x = query_17_cast_fp16)[name = string("transpose_27")]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor concat_12 = const()[name = string("concat_12"), val = tensor([512, 1, 3, 320])]; + tensor var_691_cast_fp16 = reshape(shape = concat_12, x = linear_20_cast_fp16)[name = string("op_691_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([0])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_691_cast_fp16)[name = string("op_692_cast_fp16")]; + tensor var_693_perm_0 = const()[name = string("op_693_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_694_axes_0 = const()[name = string("op_694_axes_0"), val = tensor([-2])]; + tensor var_693_cast_fp16 = transpose(perm = var_693_perm_0, x = var_692_cast_fp16)[name = string("transpose_26")]; + tensor var_694_cast_fp16 = squeeze(axes = var_694_axes_0, x = var_693_cast_fp16)[name = string("op_694_cast_fp16")]; + tensor q_25_begin_0 = const()[name = string("q_25_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_25_end_0 = const()[name = string("q_25_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_25_end_mask_0 = const()[name = string("q_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_25_squeeze_mask_0 = const()[name = string("q_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_25_cast_fp16 = slice_by_index(begin = q_25_begin_0, end = q_25_end_0, end_mask = q_25_end_mask_0, squeeze_mask = q_25_squeeze_mask_0, x = var_694_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor k_25_begin_0 = const()[name = string("k_25_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_25_end_0 = const()[name = string("k_25_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_25_end_mask_0 = const()[name = string("k_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_25_squeeze_mask_0 = const()[name = string("k_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_25_cast_fp16 = slice_by_index(begin = k_25_begin_0, end = k_25_end_0, end_mask = k_25_end_mask_0, squeeze_mask = k_25_squeeze_mask_0, x = var_694_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor v_25_begin_0 = const()[name = string("v_25_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_25_end_0 = const()[name = string("v_25_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_25_end_mask_0 = const()[name = string("v_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_25_squeeze_mask_0 = const()[name = string("v_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_25_cast_fp16 = slice_by_index(begin = v_25_begin_0, end = v_25_end_0, end_mask = v_25_end_mask_0, squeeze_mask = v_25_squeeze_mask_0, x = var_694_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor var_702 = const()[name = string("op_702"), val = tensor([512, 8, 40])]; + tensor var_703_cast_fp16 = reshape(shape = var_702, x = q_25_cast_fp16)[name = string("op_703_cast_fp16")]; + tensor q_27_perm_0 = const()[name = string("q_27_perm_0"), val = tensor([1, 0, 2])]; + tensor var_709 = const()[name = string("op_709"), val = tensor([512, 8, 40])]; + tensor var_710_cast_fp16 = reshape(shape = var_709, x = k_25_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor k_27_perm_0 = const()[name = string("k_27_perm_0"), val = tensor([1, 0, 2])]; + tensor var_716 = const()[name = string("op_716"), val = tensor([512, 8, 40])]; + tensor var_717_cast_fp16 = reshape(shape = var_716, x = v_25_cast_fp16)[name = string("op_717_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([1, 0, 2])]; + tensor var_731 = const()[name = string("op_731"), val = tensor([1, 8, 512, 40])]; + tensor q_27_cast_fp16 = transpose(perm = q_27_perm_0, x = var_703_cast_fp16)[name = string("transpose_25")]; + tensor q_29_cast_fp16 = reshape(shape = var_731, x = q_27_cast_fp16)[name = string("q_29_cast_fp16")]; + tensor var_733 = const()[name = string("op_733"), val = tensor([1, 8, 512, 40])]; + tensor k_27_cast_fp16 = transpose(perm = k_27_perm_0, x = var_710_cast_fp16)[name = string("transpose_24")]; + tensor k_29_cast_fp16 = reshape(shape = var_733, x = k_27_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor var_735 = const()[name = string("op_735"), val = tensor([1, 8, 512, 40])]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_717_cast_fp16)[name = string("transpose_23")]; + tensor v_29_cast_fp16 = reshape(shape = var_735, x = v_27_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor attn_output_33_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_29_cast_fp16, query = q_29_cast_fp16, value = v_29_cast_fp16)[name = string("attn_output_33_cast_fp16")]; + tensor var_738 = const()[name = string("op_738"), val = tensor([2, 0, 1, 3])]; + tensor var_743 = const()[name = string("op_743"), val = tensor([512, 320])]; + tensor var_739_cast_fp16 = transpose(perm = var_738, x = attn_output_33_cast_fp16)[name = string("transpose_22")]; + tensor attn_output_35_cast_fp16 = reshape(shape = var_743, x = var_739_cast_fp16)[name = string("attn_output_35_cast_fp16")]; + tensor module_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5811968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5914432))))[name = string("module_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5915136)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_35_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_747 = const()[name = string("op_747"), val = tensor([512, 1, 320])]; + tensor attn_output_39_cast_fp16 = reshape(shape = var_747, x = linear_21_cast_fp16)[name = string("attn_output_39_cast_fp16")]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([1, 0, 2])]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = attn_output_39_cast_fp16)[name = string("transpose_21")]; + tensor input_73_cast_fp16 = add(x = input_69_cast_fp16, y = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_4_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5915840)))]; + tensor module_model_encoder_layers_4_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5916544)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_model_encoder_layers_4_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_4_norm2_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5917248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6326912))))[name = string("module_model_encoder_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_4_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6329536)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_layers_4_linear1_bias_to_fp16, weight = module_model_encoder_layers_4_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_77_mode_0 = const()[name = string("input_77_mode_0"), val = string("EXACT")]; + tensor input_77_cast_fp16 = gelu(mode = input_77_mode_0, x = linear_22_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6332160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6741824))))[name = string("module_model_encoder_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_4_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6742528)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_layers_4_linear2_bias_to_fp16, weight = module_model_encoder_layers_4_linear2_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_73_cast_fp16, y = linear_23_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_5_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6743232)))]; + tensor module_model_encoder_layers_5_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6743936)))]; + tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_model_encoder_layers_5_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_5_norm1_weight_to_fp16, x = input_83_cast_fp16)[name = string("query_21_cast_fp16")]; + tensor query_23_perm_0 = const()[name = string("query_23_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6744640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7051904))))[name = string("module_model_encoder_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7053888)))]; + tensor query_23_cast_fp16 = transpose(perm = query_23_perm_0, x = query_21_cast_fp16)[name = string("transpose_20")]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor concat_14 = const()[name = string("concat_14"), val = tensor([512, 1, 3, 320])]; + tensor var_798_cast_fp16 = reshape(shape = concat_14, x = linear_24_cast_fp16)[name = string("op_798_cast_fp16")]; + tensor var_799_axes_0 = const()[name = string("op_799_axes_0"), val = tensor([0])]; + tensor var_799_cast_fp16 = expand_dims(axes = var_799_axes_0, x = var_798_cast_fp16)[name = string("op_799_cast_fp16")]; + tensor var_800_perm_0 = const()[name = string("op_800_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_801_axes_0 = const()[name = string("op_801_axes_0"), val = tensor([-2])]; + tensor var_800_cast_fp16 = transpose(perm = var_800_perm_0, x = var_799_cast_fp16)[name = string("transpose_19")]; + tensor var_801_cast_fp16 = squeeze(axes = var_801_axes_0, x = var_800_cast_fp16)[name = string("op_801_cast_fp16")]; + tensor q_31_begin_0 = const()[name = string("q_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_31_end_0 = const()[name = string("q_31_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_31_end_mask_0 = const()[name = string("q_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_31_squeeze_mask_0 = const()[name = string("q_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_31_cast_fp16 = slice_by_index(begin = q_31_begin_0, end = q_31_end_0, end_mask = q_31_end_mask_0, squeeze_mask = q_31_squeeze_mask_0, x = var_801_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor k_31_begin_0 = const()[name = string("k_31_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_31_end_0 = const()[name = string("k_31_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_31_end_mask_0 = const()[name = string("k_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_31_squeeze_mask_0 = const()[name = string("k_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_31_cast_fp16 = slice_by_index(begin = k_31_begin_0, end = k_31_end_0, end_mask = k_31_end_mask_0, squeeze_mask = k_31_squeeze_mask_0, x = var_801_cast_fp16)[name = string("k_31_cast_fp16")]; + tensor v_31_begin_0 = const()[name = string("v_31_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_31_end_0 = const()[name = string("v_31_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_31_end_mask_0 = const()[name = string("v_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_31_squeeze_mask_0 = const()[name = string("v_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_31_cast_fp16 = slice_by_index(begin = v_31_begin_0, end = v_31_end_0, end_mask = v_31_end_mask_0, squeeze_mask = v_31_squeeze_mask_0, x = var_801_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor var_809 = const()[name = string("op_809"), val = tensor([512, 8, 40])]; + tensor var_810_cast_fp16 = reshape(shape = var_809, x = q_31_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor q_33_perm_0 = const()[name = string("q_33_perm_0"), val = tensor([1, 0, 2])]; + tensor var_816 = const()[name = string("op_816"), val = tensor([512, 8, 40])]; + tensor var_817_cast_fp16 = reshape(shape = var_816, x = k_31_cast_fp16)[name = string("op_817_cast_fp16")]; + tensor k_33_perm_0 = const()[name = string("k_33_perm_0"), val = tensor([1, 0, 2])]; + tensor var_823 = const()[name = string("op_823"), val = tensor([512, 8, 40])]; + tensor var_824_cast_fp16 = reshape(shape = var_823, x = v_31_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor v_33_perm_0 = const()[name = string("v_33_perm_0"), val = tensor([1, 0, 2])]; + tensor var_838 = const()[name = string("op_838"), val = tensor([1, 8, 512, 40])]; + tensor q_33_cast_fp16 = transpose(perm = q_33_perm_0, x = var_810_cast_fp16)[name = string("transpose_18")]; + tensor q_35_cast_fp16 = reshape(shape = var_838, x = q_33_cast_fp16)[name = string("q_35_cast_fp16")]; + tensor var_840 = const()[name = string("op_840"), val = tensor([1, 8, 512, 40])]; + tensor k_33_cast_fp16 = transpose(perm = k_33_perm_0, x = var_817_cast_fp16)[name = string("transpose_17")]; + tensor k_35_cast_fp16 = reshape(shape = var_840, x = k_33_cast_fp16)[name = string("k_35_cast_fp16")]; + tensor var_842 = const()[name = string("op_842"), val = tensor([1, 8, 512, 40])]; + tensor v_33_cast_fp16 = transpose(perm = v_33_perm_0, x = var_824_cast_fp16)[name = string("transpose_16")]; + tensor v_35_cast_fp16 = reshape(shape = var_842, x = v_33_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor attn_output_41_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_35_cast_fp16, query = q_35_cast_fp16, value = v_35_cast_fp16)[name = string("attn_output_41_cast_fp16")]; + tensor var_845 = const()[name = string("op_845"), val = tensor([2, 0, 1, 3])]; + tensor var_850 = const()[name = string("op_850"), val = tensor([512, 320])]; + tensor var_846_cast_fp16 = transpose(perm = var_845, x = attn_output_41_cast_fp16)[name = string("transpose_15")]; + tensor attn_output_43_cast_fp16 = reshape(shape = var_850, x = var_846_cast_fp16)[name = string("attn_output_43_cast_fp16")]; + tensor module_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7055872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7158336))))[name = string("module_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159040)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_43_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_854 = const()[name = string("op_854"), val = tensor([512, 1, 320])]; + tensor attn_output_47_cast_fp16 = reshape(shape = var_854, x = linear_25_cast_fp16)[name = string("attn_output_47_cast_fp16")]; + tensor input_85_perm_0 = const()[name = string("input_85_perm_0"), val = tensor([1, 0, 2])]; + tensor input_85_cast_fp16 = transpose(perm = input_85_perm_0, x = attn_output_47_cast_fp16)[name = string("transpose_14")]; + tensor input_87_cast_fp16 = add(x = input_83_cast_fp16, y = input_85_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor input_89_axes_0 = const()[name = string("input_89_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_5_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159744)))]; + tensor module_model_encoder_layers_5_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7160448)))]; + tensor input_89_cast_fp16 = layer_norm(axes = input_89_axes_0, beta = module_model_encoder_layers_5_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_5_norm2_weight_to_fp16, x = input_87_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7161152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7570816))))[name = string("module_model_encoder_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_5_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7573440)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_layers_5_linear1_bias_to_fp16, weight = module_model_encoder_layers_5_linear1_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_26_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = linear_26_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7576064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7985728))))[name = string("module_model_encoder_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_5_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7986432)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_layers_5_linear2_bias_to_fp16, weight = module_model_encoder_layers_5_linear2_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_87_cast_fp16, y = linear_27_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_6_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7987136)))]; + tensor module_model_encoder_layers_6_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7987840)))]; + tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_model_encoder_layers_6_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_6_norm1_weight_to_fp16, x = input_97_cast_fp16)[name = string("query_25_cast_fp16")]; + tensor query_27_perm_0 = const()[name = string("query_27_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7988544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8295808))))[name = string("module_model_encoder_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8297792)))]; + tensor query_27_cast_fp16 = transpose(perm = query_27_perm_0, x = query_25_cast_fp16)[name = string("transpose_13")]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor concat_16 = const()[name = string("concat_16"), val = tensor([512, 1, 3, 320])]; + tensor var_905_cast_fp16 = reshape(shape = concat_16, x = linear_28_cast_fp16)[name = string("op_905_cast_fp16")]; + tensor var_906_axes_0 = const()[name = string("op_906_axes_0"), val = tensor([0])]; + tensor var_906_cast_fp16 = expand_dims(axes = var_906_axes_0, x = var_905_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_907_perm_0 = const()[name = string("op_907_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-2])]; + tensor var_907_cast_fp16 = transpose(perm = var_907_perm_0, x = var_906_cast_fp16)[name = string("transpose_12")]; + tensor var_908_cast_fp16 = squeeze(axes = var_908_axes_0, x = var_907_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor q_37_begin_0 = const()[name = string("q_37_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_37_end_0 = const()[name = string("q_37_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_37_end_mask_0 = const()[name = string("q_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_37_squeeze_mask_0 = const()[name = string("q_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_37_cast_fp16 = slice_by_index(begin = q_37_begin_0, end = q_37_end_0, end_mask = q_37_end_mask_0, squeeze_mask = q_37_squeeze_mask_0, x = var_908_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor k_37_begin_0 = const()[name = string("k_37_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_37_end_0 = const()[name = string("k_37_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_37_end_mask_0 = const()[name = string("k_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_37_squeeze_mask_0 = const()[name = string("k_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_37_cast_fp16 = slice_by_index(begin = k_37_begin_0, end = k_37_end_0, end_mask = k_37_end_mask_0, squeeze_mask = k_37_squeeze_mask_0, x = var_908_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor v_37_begin_0 = const()[name = string("v_37_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_37_end_0 = const()[name = string("v_37_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_37_end_mask_0 = const()[name = string("v_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_37_squeeze_mask_0 = const()[name = string("v_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_37_cast_fp16 = slice_by_index(begin = v_37_begin_0, end = v_37_end_0, end_mask = v_37_end_mask_0, squeeze_mask = v_37_squeeze_mask_0, x = var_908_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor var_916 = const()[name = string("op_916"), val = tensor([512, 8, 40])]; + tensor var_917_cast_fp16 = reshape(shape = var_916, x = q_37_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor q_39_perm_0 = const()[name = string("q_39_perm_0"), val = tensor([1, 0, 2])]; + tensor var_923 = const()[name = string("op_923"), val = tensor([512, 8, 40])]; + tensor var_924_cast_fp16 = reshape(shape = var_923, x = k_37_cast_fp16)[name = string("op_924_cast_fp16")]; + tensor k_39_perm_0 = const()[name = string("k_39_perm_0"), val = tensor([1, 0, 2])]; + tensor var_930 = const()[name = string("op_930"), val = tensor([512, 8, 40])]; + tensor var_931_cast_fp16 = reshape(shape = var_930, x = v_37_cast_fp16)[name = string("op_931_cast_fp16")]; + tensor v_39_perm_0 = const()[name = string("v_39_perm_0"), val = tensor([1, 0, 2])]; + tensor var_945 = const()[name = string("op_945"), val = tensor([1, 8, 512, 40])]; + tensor q_39_cast_fp16 = transpose(perm = q_39_perm_0, x = var_917_cast_fp16)[name = string("transpose_11")]; + tensor q_41_cast_fp16 = reshape(shape = var_945, x = q_39_cast_fp16)[name = string("q_41_cast_fp16")]; + tensor var_947 = const()[name = string("op_947"), val = tensor([1, 8, 512, 40])]; + tensor k_39_cast_fp16 = transpose(perm = k_39_perm_0, x = var_924_cast_fp16)[name = string("transpose_10")]; + tensor k_41_cast_fp16 = reshape(shape = var_947, x = k_39_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor var_949 = const()[name = string("op_949"), val = tensor([1, 8, 512, 40])]; + tensor v_39_cast_fp16 = transpose(perm = v_39_perm_0, x = var_931_cast_fp16)[name = string("transpose_9")]; + tensor v_41_cast_fp16 = reshape(shape = var_949, x = v_39_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor attn_output_49_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_41_cast_fp16, query = q_41_cast_fp16, value = v_41_cast_fp16)[name = string("attn_output_49_cast_fp16")]; + tensor var_952 = const()[name = string("op_952"), val = tensor([2, 0, 1, 3])]; + tensor var_957 = const()[name = string("op_957"), val = tensor([512, 320])]; + tensor var_953_cast_fp16 = transpose(perm = var_952, x = attn_output_49_cast_fp16)[name = string("transpose_8")]; + tensor attn_output_51_cast_fp16 = reshape(shape = var_957, x = var_953_cast_fp16)[name = string("attn_output_51_cast_fp16")]; + tensor module_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8299776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8402240))))[name = string("module_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8402944)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_51_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_961 = const()[name = string("op_961"), val = tensor([512, 1, 320])]; + tensor attn_output_55_cast_fp16 = reshape(shape = var_961, x = linear_29_cast_fp16)[name = string("attn_output_55_cast_fp16")]; + tensor input_99_perm_0 = const()[name = string("input_99_perm_0"), val = tensor([1, 0, 2])]; + tensor input_99_cast_fp16 = transpose(perm = input_99_perm_0, x = attn_output_55_cast_fp16)[name = string("transpose_7")]; + tensor input_101_cast_fp16 = add(x = input_97_cast_fp16, y = input_99_cast_fp16)[name = string("input_101_cast_fp16")]; + tensor input_103_axes_0 = const()[name = string("input_103_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_6_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8403648)))]; + tensor module_model_encoder_layers_6_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8404352)))]; + tensor input_103_cast_fp16 = layer_norm(axes = input_103_axes_0, beta = module_model_encoder_layers_6_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_6_norm2_weight_to_fp16, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8405056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8814720))))[name = string("module_model_encoder_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_6_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8817344)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_layers_6_linear1_bias_to_fp16, weight = module_model_encoder_layers_6_linear1_weight_to_fp16_quantized, x = input_103_cast_fp16)[name = string("linear_30_cast_fp16")]; + string input_105_mode_0 = const()[name = string("input_105_mode_0"), val = string("EXACT")]; + tensor input_105_cast_fp16 = gelu(mode = input_105_mode_0, x = linear_30_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8819968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9229632))))[name = string("module_model_encoder_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_6_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9230336)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_layers_6_linear2_bias_to_fp16, weight = module_model_encoder_layers_6_linear2_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = input_101_cast_fp16, y = linear_31_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_7_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9231040)))]; + tensor module_model_encoder_layers_7_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9231744)))]; + tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_model_encoder_layers_7_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_7_norm1_weight_to_fp16, x = input_111_cast_fp16)[name = string("query_29_cast_fp16")]; + tensor query_perm_0 = const()[name = string("query_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9232448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9539712))))[name = string("module_model_encoder_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9541696)))]; + tensor query_cast_fp16 = transpose(perm = query_perm_0, x = query_29_cast_fp16)[name = string("transpose_6")]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor concat_18 = const()[name = string("concat_18"), val = tensor([512, 1, 3, 320])]; + tensor var_1012_cast_fp16 = reshape(shape = concat_18, x = linear_32_cast_fp16)[name = string("op_1012_cast_fp16")]; + tensor var_1013_axes_0 = const()[name = string("op_1013_axes_0"), val = tensor([0])]; + tensor var_1013_cast_fp16 = expand_dims(axes = var_1013_axes_0, x = var_1012_cast_fp16)[name = string("op_1013_cast_fp16")]; + tensor var_1014_perm_0 = const()[name = string("op_1014_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_1015_axes_0 = const()[name = string("op_1015_axes_0"), val = tensor([-2])]; + tensor var_1014_cast_fp16 = transpose(perm = var_1014_perm_0, x = var_1013_cast_fp16)[name = string("transpose_5")]; + tensor var_1015_cast_fp16 = squeeze(axes = var_1015_axes_0, x = var_1014_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor q_43_begin_0 = const()[name = string("q_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_43_end_0 = const()[name = string("q_43_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_43_end_mask_0 = const()[name = string("q_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_43_squeeze_mask_0 = const()[name = string("q_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_43_cast_fp16 = slice_by_index(begin = q_43_begin_0, end = q_43_end_0, end_mask = q_43_end_mask_0, squeeze_mask = q_43_squeeze_mask_0, x = var_1015_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor k_43_begin_0 = const()[name = string("k_43_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_43_end_0 = const()[name = string("k_43_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_43_end_mask_0 = const()[name = string("k_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_43_squeeze_mask_0 = const()[name = string("k_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_43_cast_fp16 = slice_by_index(begin = k_43_begin_0, end = k_43_end_0, end_mask = k_43_end_mask_0, squeeze_mask = k_43_squeeze_mask_0, x = var_1015_cast_fp16)[name = string("k_43_cast_fp16")]; + tensor v_43_begin_0 = const()[name = string("v_43_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_43_end_0 = const()[name = string("v_43_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_43_end_mask_0 = const()[name = string("v_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_43_squeeze_mask_0 = const()[name = string("v_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_43_cast_fp16 = slice_by_index(begin = v_43_begin_0, end = v_43_end_0, end_mask = v_43_end_mask_0, squeeze_mask = v_43_squeeze_mask_0, x = var_1015_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor var_1023 = const()[name = string("op_1023"), val = tensor([512, 8, 40])]; + tensor var_1024_cast_fp16 = reshape(shape = var_1023, x = q_43_cast_fp16)[name = string("op_1024_cast_fp16")]; + tensor q_45_perm_0 = const()[name = string("q_45_perm_0"), val = tensor([1, 0, 2])]; + tensor var_1030 = const()[name = string("op_1030"), val = tensor([512, 8, 40])]; + tensor var_1031_cast_fp16 = reshape(shape = var_1030, x = k_43_cast_fp16)[name = string("op_1031_cast_fp16")]; + tensor k_45_perm_0 = const()[name = string("k_45_perm_0"), val = tensor([1, 0, 2])]; + tensor var_1037 = const()[name = string("op_1037"), val = tensor([512, 8, 40])]; + tensor var_1038_cast_fp16 = reshape(shape = var_1037, x = v_43_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor v_45_perm_0 = const()[name = string("v_45_perm_0"), val = tensor([1, 0, 2])]; + tensor var_1052 = const()[name = string("op_1052"), val = tensor([1, 8, 512, 40])]; + tensor q_45_cast_fp16 = transpose(perm = q_45_perm_0, x = var_1024_cast_fp16)[name = string("transpose_4")]; + tensor q_cast_fp16 = reshape(shape = var_1052, x = q_45_cast_fp16)[name = string("q_cast_fp16")]; + tensor var_1054 = const()[name = string("op_1054"), val = tensor([1, 8, 512, 40])]; + tensor k_45_cast_fp16 = transpose(perm = k_45_perm_0, x = var_1031_cast_fp16)[name = string("transpose_3")]; + tensor k_cast_fp16 = reshape(shape = var_1054, x = k_45_cast_fp16)[name = string("k_cast_fp16")]; + tensor var_1056 = const()[name = string("op_1056"), val = tensor([1, 8, 512, 40])]; + tensor v_45_cast_fp16 = transpose(perm = v_45_perm_0, x = var_1038_cast_fp16)[name = string("transpose_2")]; + tensor v_cast_fp16 = reshape(shape = var_1056, x = v_45_cast_fp16)[name = string("v_cast_fp16")]; + tensor attn_output_57_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_cast_fp16, query = q_cast_fp16, value = v_cast_fp16)[name = string("attn_output_57_cast_fp16")]; + tensor var_1059 = const()[name = string("op_1059"), val = tensor([2, 0, 1, 3])]; + tensor var_1064 = const()[name = string("op_1064"), val = tensor([512, 320])]; + tensor var_1060_cast_fp16 = transpose(perm = var_1059, x = attn_output_57_cast_fp16)[name = string("transpose_1")]; + tensor attn_output_59_cast_fp16 = reshape(shape = var_1064, x = var_1060_cast_fp16)[name = string("attn_output_59_cast_fp16")]; + tensor module_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9543680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9646144))))[name = string("module_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9646848)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_59_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_1068 = const()[name = string("op_1068"), val = tensor([512, 1, 320])]; + tensor attn_output_cast_fp16 = reshape(shape = var_1068, x = linear_33_cast_fp16)[name = string("attn_output_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([1, 0, 2])]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = attn_output_cast_fp16)[name = string("transpose_0")]; + tensor input_115_cast_fp16 = add(x = input_111_cast_fp16, y = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor input_117_axes_0 = const()[name = string("input_117_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_7_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9647552)))]; + tensor module_model_encoder_layers_7_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9648256)))]; + tensor input_117_cast_fp16 = layer_norm(axes = input_117_axes_0, beta = module_model_encoder_layers_7_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_7_norm2_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9648960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10058624))))[name = string("module_model_encoder_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_7_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10061248)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_layers_7_linear1_bias_to_fp16, weight = module_model_encoder_layers_7_linear1_weight_to_fp16_quantized, x = input_117_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("EXACT")]; + tensor input_119_cast_fp16 = gelu(mode = input_119_mode_0, x = linear_34_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10063872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10473536))))[name = string("module_model_encoder_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_7_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10474240)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_layers_7_linear2_bias_to_fp16, weight = module_model_encoder_layers_7_linear2_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_115_cast_fp16, y = linear_35_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor input_127_axes_0 = const()[name = string("input_127_axes_0"), val = tensor([-1])]; + tensor module_model_norm_weight_to_fp16 = const()[name = string("module_model_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10474944)))]; + tensor module_model_norm_bias_to_fp16 = const()[name = string("module_model_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10475648)))]; + tensor input_127_cast_fp16 = layer_norm(axes = input_127_axes_0, beta = module_model_norm_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_norm_weight_to_fp16, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor module_model_head_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10476352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10578816))))[name = string("module_model_head_0_weight_to_fp16_quantized")]; + tensor module_model_head_0_bias_to_fp16 = const()[name = string("module_model_head_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10579520)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_head_0_bias_to_fp16, weight = module_model_head_0_weight_to_fp16_quantized, x = input_127_cast_fp16)[name = string("linear_36_cast_fp16")]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("EXACT")]; + tensor input_131_cast_fp16 = gelu(mode = input_131_mode_0, x = linear_36_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_head_3_weight_to_fp16 = const()[name = string("module_model_head_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10580224)))]; + tensor module_model_head_3_bias_to_fp16 = const()[name = string("module_model_head_3_bias_to_fp16"), val = tensor([0x1.778p-1])]; + tensor linear_37_cast_fp16 = linear(bias = module_model_head_3_bias_to_fp16, weight = module_model_head_3_weight_to_fp16, x = input_131_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor log_frames_axes_0 = const()[name = string("log_frames_axes_0"), val = tensor([-1])]; + tensor log_frames_cast_fp16 = squeeze(axes = log_frames_axes_0, x = linear_37_cast_fp16)[name = string("log_frames_cast_fp16")]; + fp16 var_34_to_fp16 = const()[name = string("op_34_to_fp16"), val = fp16(-0x1p+2)]; + fp16 var_33_to_fp16 = const()[name = string("op_33_to_fp16"), val = fp16(0x1p+3)]; + tensor clip_2_cast_fp16 = clip(alpha = var_34_to_fp16, beta = var_33_to_fp16, x = log_frames_cast_fp16)[name = string("clip_2_cast_fp16")]; + tensor var_1103_cast_fp16 = exp(x = clip_2_cast_fp16)[name = string("op_1103_cast_fp16")]; + fp16 var_1104_to_fp16 = const()[name = string("op_1104_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1105_cast_fp16 = sub(x = var_1103_cast_fp16, y = var_1104_to_fp16)[name = string("op_1105_cast_fp16")]; + tensor var_1106_cast_fp16 = maximum(x = var_1105_cast_fp16, y = var_51_to_fp16)[name = string("op_1106_cast_fp16")]; + string var_1106_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1106_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor durations = cast(dtype = var_1106_cast_fp16_to_fp32_dtype_0, x = var_1106_cast_fp16)[name = string("cast_1")]; + } -> (durations); +} \ No newline at end of file diff --git a/coreml/coreml/duration_predictor.mlmodelc/weights/weight.bin b/coreml/coreml/duration_predictor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ea8ec4253cdfdeca2ecc04d408f173bc66dbe0fb --- /dev/null +++ b/coreml/coreml/duration_predictor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e6314ef13cc2edaffdc96840a5bf4c7e729630fb22e562f5383be48e3458038 +size 10580928 diff --git a/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin b/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..df0b04f07d2372bb3a78e44e4bf4f0c7da0ca63f --- /dev/null +++ b/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9047c70b85b77b6d386f1e6188323f82daf023854c2ae4c7018f8406c04320fb +size 243 diff --git a/coreml/coreml/g2p.mlmodelc/coremldata.bin b/coreml/coreml/g2p.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2ff1a68088f4fbf7352225e1ef0264f583f1a13f --- /dev/null +++ b/coreml/coreml/g2p.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f5fc8bee0186f33fa14d35a59a58ec3393676d07bdf03802a3836d806662cd03 +size 377 diff --git a/coreml/coreml/g2p.mlmodelc/model.mil b/coreml/coreml/g2p.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..8ca2158e00759b1324097247d1902774774b26a2 --- /dev/null +++ b/coreml/coreml/g2p.mlmodelc/model.mil @@ -0,0 +1,2100 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func g2p(tensor text) { + int32 var_24 = const()[name = string("op_24"), val = int32(-1)]; + int32 var_26 = const()[name = string("op_26"), val = int32(0)]; + tensor src_pad_mask = equal(x = text, y = var_26)[name = string("src_pad_mask")]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor module_embedding_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257408))))[name = string("module_embedding_weight_to_fp16_quantized")]; + string text_to_int16_dtype_0 = const()[name = string("text_to_int16_dtype_0"), val = string("int16")]; + string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_to_int16 = cast(dtype = text_to_int16_dtype_0, x = text)[name = string("cast_6")]; + tensor cast_49 = cast(dtype = cast_49_dtype_0, x = text_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_49, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(402)]; + tensor add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_3")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(402)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 x_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_1, validate_indices = x_1_validate_indices_0, x = module_embedding_weight_to_fp16_quantized)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor op_286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27017920))), scale = tensor([[[0x1.724p-7]]]))[name = string("op_286_to_fp16_quantized")]; + tensor input_1_cast_fp16 = add(x = x_1_cast_fp16_cast_uint16_cast_uint16, y = op_286_to_fp16_quantized)[name = string("input_1_cast_fp16")]; + tensor module_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528192))))[name = string("module_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1532096)))]; + tensor linear_0_cast_fp16 = linear(bias = module_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_292_split_sizes_0 = const()[name = string("op_292_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_292_axis_0 = const()[name = string("op_292_axis_0"), val = int32(-1)]; + tensor var_292_cast_fp16_0, tensor var_292_cast_fp16_1, tensor var_292_cast_fp16_2 = split(axis = var_292_axis_0, split_sizes = var_292_split_sizes_0, x = linear_0_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor var_295 = const()[name = string("op_295"), val = tensor([1, 512, 4, 160])]; + tensor var_296_cast_fp16 = reshape(shape = var_295, x = var_292_cast_fp16_0)[name = string("op_296_cast_fp16")]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 512, 4, 160])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = var_292_cast_fp16_1)[name = string("op_299_cast_fp16")]; + tensor var_301 = const()[name = string("op_301"), val = tensor([1, 512, 4, 160])]; + tensor var_302_cast_fp16 = reshape(shape = var_301, x = var_292_cast_fp16_2)[name = string("op_302_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_305_transpose_x_0 = const()[name = string("op_305_transpose_x_0"), val = bool(false)]; + bool var_305_transpose_y_0 = const()[name = string("op_305_transpose_y_0"), val = bool(false)]; + tensor transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_299_cast_fp16)[name = string("transpose_78")]; + tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_296_cast_fp16)[name = string("transpose_79")]; + tensor var_305_cast_fp16 = matmul(transpose_x = var_305_transpose_x_0, transpose_y = var_305_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_305_cast_fp16")]; + fp16 var_306_to_fp16 = const()[name = string("op_306_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_1_cast_fp16 = mul(x = var_305_cast_fp16, y = var_306_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor weights_1_cast_fp16 = softmax(axis = var_24, x = scores_1_cast_fp16)[name = string("weights_1_cast_fp16")]; + fp16 var_13_to_fp16 = const()[name = string("op_13_to_fp16"), val = fp16(0x1p+0)]; + string var_309_to_fp16_dtype_0 = const()[name = string("op_309_to_fp16_dtype_0"), val = string("fp16")]; + tensor src_pad_mask_to_fp16 = cast(dtype = var_309_to_fp16_dtype_0, x = src_pad_mask)[name = string("cast_2")]; + tensor var_310_cast_fp16 = sub(x = var_13_to_fp16, y = src_pad_mask_to_fp16)[name = string("op_310_cast_fp16")]; + tensor var_312_axes_0 = const()[name = string("op_312_axes_0"), val = tensor([1])]; + tensor var_312_cast_fp16 = expand_dims(axes = var_312_axes_0, x = var_310_cast_fp16)[name = string("op_312_cast_fp16")]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([2])]; + tensor var_313_cast_fp16 = expand_dims(axes = var_313_axes_0, x = var_312_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor weights_3_cast_fp16 = mul(x = weights_1_cast_fp16, y = var_313_cast_fp16)[name = string("weights_3_cast_fp16")]; + tensor var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor([-1])]; + bool var_317_keep_dims_0 = const()[name = string("op_317_keep_dims_0"), val = bool(true)]; + tensor var_317_cast_fp16 = reduce_sum(axes = var_317_axes_0, keep_dims = var_317_keep_dims_0, x = weights_3_cast_fp16)[name = string("op_317_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_318_cast_fp16 = maximum(x = var_317_cast_fp16, y = var_11_to_fp16)[name = string("op_318_cast_fp16")]; + tensor weights_5_cast_fp16 = real_div(x = weights_3_cast_fp16, y = var_318_cast_fp16)[name = string("weights_5_cast_fp16")]; + bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)]; + bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_302_cast_fp16)[name = string("transpose_77")]; + tensor context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = weights_5_cast_fp16, y = v_3_cast_fp16)[name = string("context_1_cast_fp16")]; + tensor var_321_perm_0 = const()[name = string("op_321_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 512, 640])]; + tensor var_321_cast_fp16 = transpose(perm = var_321_perm_0, x = context_1_cast_fp16)[name = string("transpose_76")]; + tensor context_3_cast_fp16 = reshape(shape = var_322, x = var_321_cast_fp16)[name = string("context_3_cast_fp16")]; + tensor module_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1945664))))[name = string("module_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1947008)))]; + tensor linear_1_cast_fp16 = linear(bias = module_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = context_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = input_1_cast_fp16, y = linear_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm1_weight_to_fp16 = const()[name = string("module_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1948352)))]; + tensor module_layers_0_norm1_bias_to_fp16 = const()[name = string("module_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1949696)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_5_cast_fp16 = layer_norm(axes = input_5_axes_0, beta = module_layers_0_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor module_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1951040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2770304))))[name = string("module_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_layers_0_linear1_bias_to_fp16 = const()[name = string("module_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2772928)))]; + tensor linear_2_cast_fp16 = linear(bias = module_layers_0_linear1_bias_to_fp16, weight = module_layers_0_linear1_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor input_9_cast_fp16 = relu(x = linear_2_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2775552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3594816))))[name = string("module_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_layers_0_linear2_bias_to_fp16 = const()[name = string("module_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3596160)))]; + tensor linear_3_cast_fp16 = linear(bias = module_layers_0_linear2_bias_to_fp16, weight = module_layers_0_linear2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_5_cast_fp16, y = linear_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm2_weight_to_fp16 = const()[name = string("module_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3597504)))]; + tensor module_layers_0_norm2_bias_to_fp16 = const()[name = string("module_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3598848)))]; + tensor x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = module_layers_0_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3600192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4829056))))[name = string("module_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4832960)))]; + tensor linear_4_cast_fp16 = linear(bias = module_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_345_split_sizes_0 = const()[name = string("op_345_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_345_axis_0 = const()[name = string("op_345_axis_0"), val = int32(-1)]; + tensor var_345_cast_fp16_0, tensor var_345_cast_fp16_1, tensor var_345_cast_fp16_2 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = linear_4_cast_fp16)[name = string("op_345_cast_fp16")]; + tensor var_348 = const()[name = string("op_348"), val = tensor([1, 512, 4, 160])]; + tensor var_349_cast_fp16 = reshape(shape = var_348, x = var_345_cast_fp16_0)[name = string("op_349_cast_fp16")]; + tensor var_351 = const()[name = string("op_351"), val = tensor([1, 512, 4, 160])]; + tensor var_352_cast_fp16 = reshape(shape = var_351, x = var_345_cast_fp16_1)[name = string("op_352_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 512, 4, 160])]; + tensor var_355_cast_fp16 = reshape(shape = var_354, x = var_345_cast_fp16_2)[name = string("op_355_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_358_transpose_x_0 = const()[name = string("op_358_transpose_x_0"), val = bool(false)]; + bool var_358_transpose_y_0 = const()[name = string("op_358_transpose_y_0"), val = bool(false)]; + tensor transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_352_cast_fp16)[name = string("transpose_74")]; + tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_349_cast_fp16)[name = string("transpose_75")]; + tensor var_358_cast_fp16 = matmul(transpose_x = var_358_transpose_x_0, transpose_y = var_358_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_3_cast_fp16 = mul(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("scores_3_cast_fp16")]; + tensor weights_7_cast_fp16 = softmax(axis = var_24, x = scores_3_cast_fp16)[name = string("weights_7_cast_fp16")]; + tensor weights_9_cast_fp16 = mul(x = weights_7_cast_fp16, y = var_313_cast_fp16)[name = string("weights_9_cast_fp16")]; + tensor var_370_axes_0 = const()[name = string("op_370_axes_0"), val = tensor([-1])]; + bool var_370_keep_dims_0 = const()[name = string("op_370_keep_dims_0"), val = bool(true)]; + tensor var_370_cast_fp16 = reduce_sum(axes = var_370_axes_0, keep_dims = var_370_keep_dims_0, x = weights_9_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_cast_fp16 = maximum(x = var_370_cast_fp16, y = var_11_to_fp16)[name = string("op_371_cast_fp16")]; + tensor weights_11_cast_fp16 = real_div(x = weights_9_cast_fp16, y = var_371_cast_fp16)[name = string("weights_11_cast_fp16")]; + bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)]; + bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_355_cast_fp16)[name = string("transpose_73")]; + tensor context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = weights_11_cast_fp16, y = v_7_cast_fp16)[name = string("context_5_cast_fp16")]; + tensor var_374_perm_0 = const()[name = string("op_374_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_375 = const()[name = string("op_375"), val = tensor([1, 512, 640])]; + tensor var_374_cast_fp16 = transpose(perm = var_374_perm_0, x = context_5_cast_fp16)[name = string("transpose_72")]; + tensor context_7_cast_fp16 = reshape(shape = var_375, x = var_374_cast_fp16)[name = string("context_7_cast_fp16")]; + tensor module_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4836864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5246528))))[name = string("module_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5247872)))]; + tensor linear_5_cast_fp16 = linear(bias = module_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = context_7_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_5_cast_fp16, y = linear_5_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm1_weight_to_fp16 = const()[name = string("module_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5249216)))]; + tensor module_layers_1_norm1_bias_to_fp16 = const()[name = string("module_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250560)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_layers_1_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5251904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6071168))))[name = string("module_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_layers_1_linear1_bias_to_fp16 = const()[name = string("module_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6073792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_layers_1_linear1_bias_to_fp16, weight = module_layers_1_linear1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = linear_6_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6076416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6895680))))[name = string("module_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_layers_1_linear2_bias_to_fp16 = const()[name = string("module_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6897024)))]; + tensor linear_7_cast_fp16 = linear(bias = module_layers_1_linear2_bias_to_fp16, weight = module_layers_1_linear2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = input_15_cast_fp16, y = linear_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm2_weight_to_fp16 = const()[name = string("module_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6898368)))]; + tensor module_layers_1_norm2_bias_to_fp16 = const()[name = string("module_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6899712)))]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = module_layers_1_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm2_weight_to_fp16, x = input_21_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor module_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8129920))))[name = string("module_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8133824)))]; + tensor linear_8_cast_fp16 = linear(bias = module_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = x_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_398_split_sizes_0 = const()[name = string("op_398_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_398_axis_0 = const()[name = string("op_398_axis_0"), val = int32(-1)]; + tensor var_398_cast_fp16_0, tensor var_398_cast_fp16_1, tensor var_398_cast_fp16_2 = split(axis = var_398_axis_0, split_sizes = var_398_split_sizes_0, x = linear_8_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_401 = const()[name = string("op_401"), val = tensor([1, 512, 4, 160])]; + tensor var_402_cast_fp16 = reshape(shape = var_401, x = var_398_cast_fp16_0)[name = string("op_402_cast_fp16")]; + tensor var_404 = const()[name = string("op_404"), val = tensor([1, 512, 4, 160])]; + tensor var_405_cast_fp16 = reshape(shape = var_404, x = var_398_cast_fp16_1)[name = string("op_405_cast_fp16")]; + tensor var_407 = const()[name = string("op_407"), val = tensor([1, 512, 4, 160])]; + tensor var_408_cast_fp16 = reshape(shape = var_407, x = var_398_cast_fp16_2)[name = string("op_408_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_411_transpose_x_0 = const()[name = string("op_411_transpose_x_0"), val = bool(false)]; + bool var_411_transpose_y_0 = const()[name = string("op_411_transpose_y_0"), val = bool(false)]; + tensor transpose_36_perm_0 = const()[name = string("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_37_perm_0 = const()[name = string("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_405_cast_fp16)[name = string("transpose_70")]; + tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_402_cast_fp16)[name = string("transpose_71")]; + tensor var_411_cast_fp16 = matmul(transpose_x = var_411_transpose_x_0, transpose_y = var_411_transpose_y_0, x = transpose_36, y = transpose_37)[name = string("op_411_cast_fp16")]; + fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_5_cast_fp16 = mul(x = var_411_cast_fp16, y = var_412_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor weights_13_cast_fp16 = softmax(axis = var_24, x = scores_5_cast_fp16)[name = string("weights_13_cast_fp16")]; + tensor weights_15_cast_fp16 = mul(x = weights_13_cast_fp16, y = var_313_cast_fp16)[name = string("weights_15_cast_fp16")]; + tensor var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor([-1])]; + bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)]; + tensor var_423_cast_fp16 = reduce_sum(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = weights_15_cast_fp16)[name = string("op_423_cast_fp16")]; + tensor var_424_cast_fp16 = maximum(x = var_423_cast_fp16, y = var_11_to_fp16)[name = string("op_424_cast_fp16")]; + tensor weights_17_cast_fp16 = real_div(x = weights_15_cast_fp16, y = var_424_cast_fp16)[name = string("weights_17_cast_fp16")]; + bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)]; + bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_408_cast_fp16)[name = string("transpose_69")]; + tensor context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = weights_17_cast_fp16, y = v_11_cast_fp16)[name = string("context_9_cast_fp16")]; + tensor var_427_perm_0 = const()[name = string("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 512, 640])]; + tensor var_427_cast_fp16 = transpose(perm = var_427_perm_0, x = context_9_cast_fp16)[name = string("transpose_68")]; + tensor context_11_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("context_11_cast_fp16")]; + tensor module_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8137728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8547392))))[name = string("module_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548736)))]; + tensor linear_9_cast_fp16 = linear(bias = module_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = context_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = x_7_cast_fp16, y = linear_9_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm1_weight_to_fp16 = const()[name = string("module_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550080)))]; + tensor module_layers_2_norm1_bias_to_fp16 = const()[name = string("module_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8551424)))]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = module_layers_2_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8552768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9372032))))[name = string("module_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_layers_2_linear1_bias_to_fp16 = const()[name = string("module_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9374656)))]; + tensor linear_10_cast_fp16 = linear(bias = module_layers_2_linear1_bias_to_fp16, weight = module_layers_2_linear1_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_29_cast_fp16 = relu(x = linear_10_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor module_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9377280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196544))))[name = string("module_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_layers_2_linear2_bias_to_fp16 = const()[name = string("module_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197888)))]; + tensor linear_11_cast_fp16 = linear(bias = module_layers_2_linear2_bias_to_fp16, weight = module_layers_2_linear2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_25_cast_fp16, y = linear_11_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm2_weight_to_fp16 = const()[name = string("module_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199232)))]; + tensor module_layers_2_norm2_bias_to_fp16 = const()[name = string("module_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10200576)))]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = module_layers_2_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor module_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10201920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11430784))))[name = string("module_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11434688)))]; + tensor linear_12_cast_fp16 = linear(bias = module_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = x_9_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(-1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1, tensor var_451_cast_fp16_2 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = linear_12_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_454 = const()[name = string("op_454"), val = tensor([1, 512, 4, 160])]; + tensor var_455_cast_fp16 = reshape(shape = var_454, x = var_451_cast_fp16_0)[name = string("op_455_cast_fp16")]; + tensor var_457 = const()[name = string("op_457"), val = tensor([1, 512, 4, 160])]; + tensor var_458_cast_fp16 = reshape(shape = var_457, x = var_451_cast_fp16_1)[name = string("op_458_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([1, 512, 4, 160])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = var_451_cast_fp16_2)[name = string("op_461_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_464_transpose_x_0 = const()[name = string("op_464_transpose_x_0"), val = bool(false)]; + bool var_464_transpose_y_0 = const()[name = string("op_464_transpose_y_0"), val = bool(false)]; + tensor transpose_38_perm_0 = const()[name = string("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_39_perm_0 = const()[name = string("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_458_cast_fp16)[name = string("transpose_66")]; + tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_455_cast_fp16)[name = string("transpose_67")]; + tensor var_464_cast_fp16 = matmul(transpose_x = var_464_transpose_x_0, transpose_y = var_464_transpose_y_0, x = transpose_38, y = transpose_39)[name = string("op_464_cast_fp16")]; + fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_7_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("scores_7_cast_fp16")]; + tensor weights_19_cast_fp16 = softmax(axis = var_24, x = scores_7_cast_fp16)[name = string("weights_19_cast_fp16")]; + tensor weights_21_cast_fp16 = mul(x = weights_19_cast_fp16, y = var_313_cast_fp16)[name = string("weights_21_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + bool var_476_keep_dims_0 = const()[name = string("op_476_keep_dims_0"), val = bool(true)]; + tensor var_476_cast_fp16 = reduce_sum(axes = var_476_axes_0, keep_dims = var_476_keep_dims_0, x = weights_21_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor var_477_cast_fp16 = maximum(x = var_476_cast_fp16, y = var_11_to_fp16)[name = string("op_477_cast_fp16")]; + tensor weights_23_cast_fp16 = real_div(x = weights_21_cast_fp16, y = var_477_cast_fp16)[name = string("weights_23_cast_fp16")]; + bool context_13_transpose_x_0 = const()[name = string("context_13_transpose_x_0"), val = bool(false)]; + bool context_13_transpose_y_0 = const()[name = string("context_13_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_461_cast_fp16)[name = string("transpose_65")]; + tensor context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = weights_23_cast_fp16, y = v_15_cast_fp16)[name = string("context_13_cast_fp16")]; + tensor var_480_perm_0 = const()[name = string("op_480_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_481 = const()[name = string("op_481"), val = tensor([1, 512, 640])]; + tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = context_13_cast_fp16)[name = string("transpose_64")]; + tensor context_15_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("context_15_cast_fp16")]; + tensor module_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11438592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11848256))))[name = string("module_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11849600)))]; + tensor linear_13_cast_fp16 = linear(bias = module_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = context_15_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = x_9_cast_fp16, y = linear_13_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_axes_0 = const()[name = string("input_35_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm1_weight_to_fp16 = const()[name = string("module_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11850944)))]; + tensor module_layers_3_norm1_bias_to_fp16 = const()[name = string("module_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11852288)))]; + tensor input_35_cast_fp16 = layer_norm(axes = input_35_axes_0, beta = module_layers_3_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11853632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12672896))))[name = string("module_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_layers_3_linear1_bias_to_fp16 = const()[name = string("module_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12675520)))]; + tensor linear_14_cast_fp16 = linear(bias = module_layers_3_linear1_bias_to_fp16, weight = module_layers_3_linear1_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor input_39_cast_fp16 = relu(x = linear_14_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12678144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13497408))))[name = string("module_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_layers_3_linear2_bias_to_fp16 = const()[name = string("module_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13498752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_layers_3_linear2_bias_to_fp16, weight = module_layers_3_linear2_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm2_weight_to_fp16 = const()[name = string("module_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13500096)))]; + tensor module_layers_3_norm2_bias_to_fp16 = const()[name = string("module_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13501440)))]; + tensor x_11_cast_fp16 = layer_norm(axes = x_11_axes_0, beta = module_layers_3_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm2_weight_to_fp16, x = input_41_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13502784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14731648))))[name = string("module_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14735552)))]; + tensor linear_16_cast_fp16 = linear(bias = module_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_504_split_sizes_0 = const()[name = string("op_504_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_504_axis_0 = const()[name = string("op_504_axis_0"), val = int32(-1)]; + tensor var_504_cast_fp16_0, tensor var_504_cast_fp16_1, tensor var_504_cast_fp16_2 = split(axis = var_504_axis_0, split_sizes = var_504_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_504_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 512, 4, 160])]; + tensor var_508_cast_fp16 = reshape(shape = var_507, x = var_504_cast_fp16_0)[name = string("op_508_cast_fp16")]; + tensor var_510 = const()[name = string("op_510"), val = tensor([1, 512, 4, 160])]; + tensor var_511_cast_fp16 = reshape(shape = var_510, x = var_504_cast_fp16_1)[name = string("op_511_cast_fp16")]; + tensor var_513 = const()[name = string("op_513"), val = tensor([1, 512, 4, 160])]; + tensor var_514_cast_fp16 = reshape(shape = var_513, x = var_504_cast_fp16_2)[name = string("op_514_cast_fp16")]; + tensor v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_517_transpose_x_0 = const()[name = string("op_517_transpose_x_0"), val = bool(false)]; + bool var_517_transpose_y_0 = const()[name = string("op_517_transpose_y_0"), val = bool(false)]; + tensor transpose_40_perm_0 = const()[name = string("transpose_40_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_41_perm_0 = const()[name = string("transpose_41_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_41 = transpose(perm = transpose_41_perm_0, x = var_511_cast_fp16)[name = string("transpose_62")]; + tensor transpose_40 = transpose(perm = transpose_40_perm_0, x = var_508_cast_fp16)[name = string("transpose_63")]; + tensor var_517_cast_fp16 = matmul(transpose_x = var_517_transpose_x_0, transpose_y = var_517_transpose_y_0, x = transpose_40, y = transpose_41)[name = string("op_517_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_9_cast_fp16 = mul(x = var_517_cast_fp16, y = var_518_to_fp16)[name = string("scores_9_cast_fp16")]; + tensor weights_25_cast_fp16 = softmax(axis = var_24, x = scores_9_cast_fp16)[name = string("weights_25_cast_fp16")]; + tensor weights_27_cast_fp16 = mul(x = weights_25_cast_fp16, y = var_313_cast_fp16)[name = string("weights_27_cast_fp16")]; + tensor var_529_axes_0 = const()[name = string("op_529_axes_0"), val = tensor([-1])]; + bool var_529_keep_dims_0 = const()[name = string("op_529_keep_dims_0"), val = bool(true)]; + tensor var_529_cast_fp16 = reduce_sum(axes = var_529_axes_0, keep_dims = var_529_keep_dims_0, x = weights_27_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = maximum(x = var_529_cast_fp16, y = var_11_to_fp16)[name = string("op_530_cast_fp16")]; + tensor weights_29_cast_fp16 = real_div(x = weights_27_cast_fp16, y = var_530_cast_fp16)[name = string("weights_29_cast_fp16")]; + bool context_17_transpose_x_0 = const()[name = string("context_17_transpose_x_0"), val = bool(false)]; + bool context_17_transpose_y_0 = const()[name = string("context_17_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = v_19_perm_0, x = var_514_cast_fp16)[name = string("transpose_61")]; + tensor context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = weights_29_cast_fp16, y = v_19_cast_fp16)[name = string("context_17_cast_fp16")]; + tensor var_533_perm_0 = const()[name = string("op_533_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 512, 640])]; + tensor var_533_cast_fp16 = transpose(perm = var_533_perm_0, x = context_17_cast_fp16)[name = string("transpose_60")]; + tensor context_19_cast_fp16 = reshape(shape = var_534, x = var_533_cast_fp16)[name = string("context_19_cast_fp16")]; + tensor module_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14739456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15149120))))[name = string("module_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15150464)))]; + tensor linear_17_cast_fp16 = linear(bias = module_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = context_19_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = x_11_cast_fp16, y = linear_17_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm1_weight_to_fp16 = const()[name = string("module_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15151808)))]; + tensor module_layers_4_norm1_bias_to_fp16 = const()[name = string("module_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15153152)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = module_layers_4_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15973760))))[name = string("module_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_layers_4_linear1_bias_to_fp16 = const()[name = string("module_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15976384)))]; + tensor linear_18_cast_fp16 = linear(bias = module_layers_4_linear1_bias_to_fp16, weight = module_layers_4_linear1_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = linear_18_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16798272))))[name = string("module_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_layers_4_linear2_bias_to_fp16 = const()[name = string("module_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16799616)))]; + tensor linear_19_cast_fp16 = linear(bias = module_layers_4_linear2_bias_to_fp16, weight = module_layers_4_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_45_cast_fp16, y = linear_19_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm2_weight_to_fp16 = const()[name = string("module_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16800960)))]; + tensor module_layers_4_norm2_bias_to_fp16 = const()[name = string("module_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16802304)))]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = module_layers_4_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm2_weight_to_fp16, x = input_51_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor module_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16803648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18032512))))[name = string("module_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor linear_20_cast_fp16 = linear(bias = module_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = x_13_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_557_split_sizes_0 = const()[name = string("op_557_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_557_axis_0 = const()[name = string("op_557_axis_0"), val = int32(-1)]; + tensor var_557_cast_fp16_0, tensor var_557_cast_fp16_1, tensor var_557_cast_fp16_2 = split(axis = var_557_axis_0, split_sizes = var_557_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_560 = const()[name = string("op_560"), val = tensor([1, 512, 4, 160])]; + tensor var_561_cast_fp16 = reshape(shape = var_560, x = var_557_cast_fp16_0)[name = string("op_561_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([1, 512, 4, 160])]; + tensor var_564_cast_fp16 = reshape(shape = var_563, x = var_557_cast_fp16_1)[name = string("op_564_cast_fp16")]; + tensor var_566 = const()[name = string("op_566"), val = tensor([1, 512, 4, 160])]; + tensor var_567_cast_fp16 = reshape(shape = var_566, x = var_557_cast_fp16_2)[name = string("op_567_cast_fp16")]; + tensor v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_570_transpose_x_0 = const()[name = string("op_570_transpose_x_0"), val = bool(false)]; + bool var_570_transpose_y_0 = const()[name = string("op_570_transpose_y_0"), val = bool(false)]; + tensor transpose_42_perm_0 = const()[name = string("transpose_42_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_43_perm_0 = const()[name = string("transpose_43_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_43 = transpose(perm = transpose_43_perm_0, x = var_564_cast_fp16)[name = string("transpose_58")]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = var_561_cast_fp16)[name = string("transpose_59")]; + tensor var_570_cast_fp16 = matmul(transpose_x = var_570_transpose_x_0, transpose_y = var_570_transpose_y_0, x = transpose_42, y = transpose_43)[name = string("op_570_cast_fp16")]; + fp16 var_571_to_fp16 = const()[name = string("op_571_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_11_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = string("scores_11_cast_fp16")]; + tensor weights_31_cast_fp16 = softmax(axis = var_24, x = scores_11_cast_fp16)[name = string("weights_31_cast_fp16")]; + tensor weights_33_cast_fp16 = mul(x = weights_31_cast_fp16, y = var_313_cast_fp16)[name = string("weights_33_cast_fp16")]; + tensor var_582_axes_0 = const()[name = string("op_582_axes_0"), val = tensor([-1])]; + bool var_582_keep_dims_0 = const()[name = string("op_582_keep_dims_0"), val = bool(true)]; + tensor var_582_cast_fp16 = reduce_sum(axes = var_582_axes_0, keep_dims = var_582_keep_dims_0, x = weights_33_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = maximum(x = var_582_cast_fp16, y = var_11_to_fp16)[name = string("op_583_cast_fp16")]; + tensor weights_35_cast_fp16 = real_div(x = weights_33_cast_fp16, y = var_583_cast_fp16)[name = string("weights_35_cast_fp16")]; + bool context_21_transpose_x_0 = const()[name = string("context_21_transpose_x_0"), val = bool(false)]; + bool context_21_transpose_y_0 = const()[name = string("context_21_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = v_23_perm_0, x = var_567_cast_fp16)[name = string("transpose_57")]; + tensor context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = weights_35_cast_fp16, y = v_23_cast_fp16)[name = string("context_21_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 512, 640])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = context_21_cast_fp16)[name = string("transpose_56")]; + tensor context_23_cast_fp16 = reshape(shape = var_587, x = var_586_cast_fp16)[name = string("context_23_cast_fp16")]; + tensor module_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18040320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18449984))))[name = string("module_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18451328)))]; + tensor linear_21_cast_fp16 = linear(bias = module_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = context_23_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = x_13_cast_fp16, y = linear_21_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm1_weight_to_fp16 = const()[name = string("module_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18452672)))]; + tensor module_layers_5_norm1_bias_to_fp16 = const()[name = string("module_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18454016)))]; + tensor input_55_cast_fp16 = layer_norm(axes = input_55_axes_0, beta = module_layers_5_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm1_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18455360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19274624))))[name = string("module_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_layers_5_linear1_bias_to_fp16 = const()[name = string("module_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19277248)))]; + tensor linear_22_cast_fp16 = linear(bias = module_layers_5_linear1_bias_to_fp16, weight = module_layers_5_linear1_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_59_cast_fp16 = relu(x = linear_22_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19279872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20099136))))[name = string("module_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_layers_5_linear2_bias_to_fp16 = const()[name = string("module_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20100480)))]; + tensor linear_23_cast_fp16 = linear(bias = module_layers_5_linear2_bias_to_fp16, weight = module_layers_5_linear2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_55_cast_fp16, y = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm2_weight_to_fp16 = const()[name = string("module_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20101824)))]; + tensor module_layers_5_norm2_bias_to_fp16 = const()[name = string("module_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20103168)))]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, beta = module_layers_5_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor module_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21333376))))[name = string("module_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21337280)))]; + tensor linear_24_cast_fp16 = linear(bias = module_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = x_15_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_610_split_sizes_0 = const()[name = string("op_610_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_610_axis_0 = const()[name = string("op_610_axis_0"), val = int32(-1)]; + tensor var_610_cast_fp16_0, tensor var_610_cast_fp16_1, tensor var_610_cast_fp16_2 = split(axis = var_610_axis_0, split_sizes = var_610_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor var_613 = const()[name = string("op_613"), val = tensor([1, 512, 4, 160])]; + tensor var_614_cast_fp16 = reshape(shape = var_613, x = var_610_cast_fp16_0)[name = string("op_614_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 512, 4, 160])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_610_cast_fp16_1)[name = string("op_617_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 512, 4, 160])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = var_610_cast_fp16_2)[name = string("op_620_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_623_transpose_x_0 = const()[name = string("op_623_transpose_x_0"), val = bool(false)]; + bool var_623_transpose_y_0 = const()[name = string("op_623_transpose_y_0"), val = bool(false)]; + tensor transpose_44_perm_0 = const()[name = string("transpose_44_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_45_perm_0 = const()[name = string("transpose_45_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_45 = transpose(perm = transpose_45_perm_0, x = var_617_cast_fp16)[name = string("transpose_54")]; + tensor transpose_44 = transpose(perm = transpose_44_perm_0, x = var_614_cast_fp16)[name = string("transpose_55")]; + tensor var_623_cast_fp16 = matmul(transpose_x = var_623_transpose_x_0, transpose_y = var_623_transpose_y_0, x = transpose_44, y = transpose_45)[name = string("op_623_cast_fp16")]; + fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_13_cast_fp16 = mul(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("scores_13_cast_fp16")]; + tensor weights_37_cast_fp16 = softmax(axis = var_24, x = scores_13_cast_fp16)[name = string("weights_37_cast_fp16")]; + tensor weights_39_cast_fp16 = mul(x = weights_37_cast_fp16, y = var_313_cast_fp16)[name = string("weights_39_cast_fp16")]; + tensor var_635_axes_0 = const()[name = string("op_635_axes_0"), val = tensor([-1])]; + bool var_635_keep_dims_0 = const()[name = string("op_635_keep_dims_0"), val = bool(true)]; + tensor var_635_cast_fp16 = reduce_sum(axes = var_635_axes_0, keep_dims = var_635_keep_dims_0, x = weights_39_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_cast_fp16 = maximum(x = var_635_cast_fp16, y = var_11_to_fp16)[name = string("op_636_cast_fp16")]; + tensor weights_41_cast_fp16 = real_div(x = weights_39_cast_fp16, y = var_636_cast_fp16)[name = string("weights_41_cast_fp16")]; + bool context_25_transpose_x_0 = const()[name = string("context_25_transpose_x_0"), val = bool(false)]; + bool context_25_transpose_y_0 = const()[name = string("context_25_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_620_cast_fp16)[name = string("transpose_53")]; + tensor context_25_cast_fp16 = matmul(transpose_x = context_25_transpose_x_0, transpose_y = context_25_transpose_y_0, x = weights_41_cast_fp16, y = v_27_cast_fp16)[name = string("context_25_cast_fp16")]; + tensor var_639_perm_0 = const()[name = string("op_639_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_640 = const()[name = string("op_640"), val = tensor([1, 512, 640])]; + tensor var_639_cast_fp16 = transpose(perm = var_639_perm_0, x = context_25_cast_fp16)[name = string("transpose_52")]; + tensor context_27_cast_fp16 = reshape(shape = var_640, x = var_639_cast_fp16)[name = string("context_27_cast_fp16")]; + tensor module_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21341184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750848))))[name = string("module_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21752192)))]; + tensor linear_25_cast_fp16 = linear(bias = module_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = context_27_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = x_15_cast_fp16, y = linear_25_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm1_weight_to_fp16 = const()[name = string("module_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21753536)))]; + tensor module_layers_6_norm1_bias_to_fp16 = const()[name = string("module_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21754880)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = module_layers_6_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21756224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22575488))))[name = string("module_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_layers_6_linear1_bias_to_fp16 = const()[name = string("module_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22578112)))]; + tensor linear_26_cast_fp16 = linear(bias = module_layers_6_linear1_bias_to_fp16, weight = module_layers_6_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = linear_26_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22580736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23400000))))[name = string("module_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_layers_6_linear2_bias_to_fp16 = const()[name = string("module_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23401344)))]; + tensor linear_27_cast_fp16 = linear(bias = module_layers_6_linear2_bias_to_fp16, weight = module_layers_6_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = input_65_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm2_weight_to_fp16 = const()[name = string("module_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23402688)))]; + tensor module_layers_6_norm2_bias_to_fp16 = const()[name = string("module_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404032)))]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, beta = module_layers_6_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm2_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_cast_fp16")]; + tensor module_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23405376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24634240))))[name = string("module_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24638144)))]; + tensor linear_28_cast_fp16 = linear(bias = module_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = x_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_663_split_sizes_0 = const()[name = string("op_663_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_663_axis_0 = const()[name = string("op_663_axis_0"), val = int32(-1)]; + tensor var_663_cast_fp16_0, tensor var_663_cast_fp16_1, tensor var_663_cast_fp16_2 = split(axis = var_663_axis_0, split_sizes = var_663_split_sizes_0, x = linear_28_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_666 = const()[name = string("op_666"), val = tensor([1, 512, 4, 160])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = var_663_cast_fp16_0)[name = string("op_667_cast_fp16")]; + tensor var_669 = const()[name = string("op_669"), val = tensor([1, 512, 4, 160])]; + tensor var_670_cast_fp16 = reshape(shape = var_669, x = var_663_cast_fp16_1)[name = string("op_670_cast_fp16")]; + tensor var_672 = const()[name = string("op_672"), val = tensor([1, 512, 4, 160])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = var_663_cast_fp16_2)[name = string("op_673_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_676_transpose_x_0 = const()[name = string("op_676_transpose_x_0"), val = bool(false)]; + bool var_676_transpose_y_0 = const()[name = string("op_676_transpose_y_0"), val = bool(false)]; + tensor transpose_46_perm_0 = const()[name = string("transpose_46_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_47_perm_0 = const()[name = string("transpose_47_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_47 = transpose(perm = transpose_47_perm_0, x = var_670_cast_fp16)[name = string("transpose_50")]; + tensor transpose_46 = transpose(perm = transpose_46_perm_0, x = var_667_cast_fp16)[name = string("transpose_51")]; + tensor var_676_cast_fp16 = matmul(transpose_x = var_676_transpose_x_0, transpose_y = var_676_transpose_y_0, x = transpose_46, y = transpose_47)[name = string("op_676_cast_fp16")]; + fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_cast_fp16 = mul(x = var_676_cast_fp16, y = var_677_to_fp16)[name = string("scores_cast_fp16")]; + tensor weights_43_cast_fp16 = softmax(axis = var_24, x = scores_cast_fp16)[name = string("weights_43_cast_fp16")]; + tensor weights_45_cast_fp16 = mul(x = weights_43_cast_fp16, y = var_313_cast_fp16)[name = string("weights_45_cast_fp16")]; + tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([-1])]; + bool var_688_keep_dims_0 = const()[name = string("op_688_keep_dims_0"), val = bool(true)]; + tensor var_688_cast_fp16 = reduce_sum(axes = var_688_axes_0, keep_dims = var_688_keep_dims_0, x = weights_45_cast_fp16)[name = string("op_688_cast_fp16")]; + tensor var_689_cast_fp16 = maximum(x = var_688_cast_fp16, y = var_11_to_fp16)[name = string("op_689_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_45_cast_fp16, y = var_689_cast_fp16)[name = string("weights_cast_fp16")]; + bool context_29_transpose_x_0 = const()[name = string("context_29_transpose_x_0"), val = bool(false)]; + bool context_29_transpose_y_0 = const()[name = string("context_29_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_673_cast_fp16)[name = string("transpose_49")]; + tensor context_29_cast_fp16 = matmul(transpose_x = context_29_transpose_x_0, transpose_y = context_29_transpose_y_0, x = weights_cast_fp16, y = v_cast_fp16)[name = string("context_29_cast_fp16")]; + tensor var_692_perm_0 = const()[name = string("op_692_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_693 = const()[name = string("op_693"), val = tensor([1, 512, 640])]; + tensor var_692_cast_fp16 = transpose(perm = var_692_perm_0, x = context_29_cast_fp16)[name = string("transpose_48")]; + tensor context_cast_fp16 = reshape(shape = var_693, x = var_692_cast_fp16)[name = string("context_cast_fp16")]; + tensor module_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24642048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25051712))))[name = string("module_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25053056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = context_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = x_cast_fp16, y = linear_29_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm1_weight_to_fp16 = const()[name = string("module_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25054400)))]; + tensor module_layers_7_norm1_bias_to_fp16 = const()[name = string("module_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25055744)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_layers_7_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm1_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25057088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25876352))))[name = string("module_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_layers_7_linear1_bias_to_fp16 = const()[name = string("module_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25878976)))]; + tensor linear_30_cast_fp16 = linear(bias = module_layers_7_linear1_bias_to_fp16, weight = module_layers_7_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_79_cast_fp16 = relu(x = linear_30_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25881600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26700864))))[name = string("module_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_layers_7_linear2_bias_to_fp16 = const()[name = string("module_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26702208)))]; + tensor linear_31_cast_fp16 = linear(bias = module_layers_7_linear2_bias_to_fp16, weight = module_layers_7_linear2_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = input_75_cast_fp16, y = linear_31_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor input_83_axes_0 = const()[name = string("input_83_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm2_weight_to_fp16 = const()[name = string("module_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26703552)))]; + tensor module_layers_7_norm2_bias_to_fp16 = const()[name = string("module_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26704896)))]; + tensor input_83_cast_fp16 = layer_norm(axes = input_83_axes_0, beta = module_layers_7_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm2_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([-1])]; + tensor module_encoder_norm_weight_to_fp16 = const()[name = string("module_encoder_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26706240)))]; + tensor module_encoder_norm_bias_to_fp16 = const()[name = string("module_encoder_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26707584)))]; + tensor input_cast_fp16 = layer_norm(axes = input_axes_0, beta = module_encoder_norm_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_encoder_norm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_cast_fp16")]; + tensor module_fc_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26708928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770432))))[name = string("module_fc_out_weight_to_fp16_quantized")]; + tensor module_fc_out_bias_to_fp16 = const()[name = string("module_fc_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770688)))]; + tensor linear_32_cast_fp16 = linear(bias = module_fc_out_bias_to_fp16, weight = module_fc_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor padded_axes_0 = const()[name = string("padded_axes_0"), val = tensor([-1])]; + tensor padded = expand_dims(axes = padded_axes_0, x = src_pad_mask)[name = string("padded")]; + tensor var_723_begin_0 = const()[name = string("op_723_begin_0"), val = tensor([0, 0, 0])]; + tensor var_723_end_0 = const()[name = string("op_723_end_0"), val = tensor([1, 512, 1])]; + tensor var_723_end_mask_0 = const()[name = string("op_723_end_mask_0"), val = tensor([true, true, false])]; + tensor var_723_cast_fp16 = slice_by_index(begin = var_723_begin_0, end = var_723_end_0, end_mask = var_723_end_mask_0, x = linear_32_cast_fp16)[name = string("op_723_cast_fp16")]; + tensor var_722_to_fp16 = const()[name = string("op_722_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27345664)))]; + tensor blank_logits_cast_fp16 = select(a = var_722_to_fp16, b = var_723_cast_fp16, cond = padded)[name = string("blank_logits_cast_fp16")]; + tensor var_725_begin_0 = const()[name = string("op_725_begin_0"), val = tensor([0, 0, 1])]; + tensor var_725_end_0 = const()[name = string("op_725_end_0"), val = tensor([1, 512, 96])]; + tensor var_725_end_mask_0 = const()[name = string("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725_cast_fp16 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = linear_32_cast_fp16)[name = string("op_725_cast_fp16")]; + fp16 var_726_value_0_to_fp16 = const()[name = string("op_726_value_0_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor var_726_cast_fp16 = fill_like(ref_tensor = var_725_cast_fp16, value = var_726_value_0_to_fp16)[name = string("op_726_cast_fp16")]; + tensor padded_after_broadcast_reps_0 = const()[name = string("padded_after_broadcast_reps_0"), val = tensor([1, 1, 95])]; + tensor padded_after_broadcast = tile(reps = padded_after_broadcast_reps_0, x = padded)[name = string("padded_after_broadcast")]; + tensor nonblank_logits_cast_fp16 = select(a = var_726_cast_fp16, b = var_725_cast_fp16, cond = padded_after_broadcast)[name = string("nonblank_logits_cast_fp16")]; + bool var_730_interleave_0 = const()[name = string("op_730_interleave_0"), val = bool(false)]; + tensor var_730_cast_fp16 = concat(axis = var_24, interleave = var_730_interleave_0, values = (blank_logits_cast_fp16, nonblank_logits_cast_fp16))[name = string("op_730_cast_fp16")]; + string var_730_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_730_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_730_cast_fp16_to_fp32_dtype_0, x = var_730_cast_fp16)[name = string("cast_1")]; + } -> (logits); + func g2p_128(tensor text) { + int32 var_24 = const()[name = string("op_24"), val = int32(-1)]; + int32 var_26 = const()[name = string("op_26"), val = int32(0)]; + tensor src_pad_mask = equal(x = text, y = var_26)[name = string("src_pad_mask")]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor module_embedding_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257408))))[name = string("module_embedding_weight_to_fp16_quantized")]; + string text_to_int16_dtype_0 = const()[name = string("text_to_int16_dtype_0"), val = string("int16")]; + string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_to_int16 = cast(dtype = text_to_int16_dtype_0, x = text)[name = string("cast_6")]; + tensor cast_49 = cast(dtype = cast_49_dtype_0, x = text_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_49, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(402)]; + tensor add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_3")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(402)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 x_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_1, validate_indices = x_1_validate_indices_0, x = module_embedding_weight_to_fp16_quantized)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor op_286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26771136))), scale = tensor([[[0x1.724p-7]]]))[name = string("op_286_to_fp16_quantized")]; + tensor input_1_cast_fp16 = add(x = x_1_cast_fp16_cast_uint16_cast_uint16, y = op_286_to_fp16_quantized)[name = string("input_1_cast_fp16")]; + tensor module_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528192))))[name = string("module_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1532096)))]; + tensor linear_0_cast_fp16 = linear(bias = module_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_292_split_sizes_0 = const()[name = string("op_292_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_292_axis_0 = const()[name = string("op_292_axis_0"), val = int32(-1)]; + tensor var_292_cast_fp16_0, tensor var_292_cast_fp16_1, tensor var_292_cast_fp16_2 = split(axis = var_292_axis_0, split_sizes = var_292_split_sizes_0, x = linear_0_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor var_295 = const()[name = string("op_295"), val = tensor([1, 128, 4, 160])]; + tensor var_296_cast_fp16 = reshape(shape = var_295, x = var_292_cast_fp16_0)[name = string("op_296_cast_fp16")]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 128, 4, 160])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = var_292_cast_fp16_1)[name = string("op_299_cast_fp16")]; + tensor var_301 = const()[name = string("op_301"), val = tensor([1, 128, 4, 160])]; + tensor var_302_cast_fp16 = reshape(shape = var_301, x = var_292_cast_fp16_2)[name = string("op_302_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_305_transpose_x_0 = const()[name = string("op_305_transpose_x_0"), val = bool(false)]; + bool var_305_transpose_y_0 = const()[name = string("op_305_transpose_y_0"), val = bool(false)]; + tensor transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_299_cast_fp16)[name = string("transpose_78")]; + tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_296_cast_fp16)[name = string("transpose_79")]; + tensor var_305_cast_fp16 = matmul(transpose_x = var_305_transpose_x_0, transpose_y = var_305_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_305_cast_fp16")]; + fp16 var_306_to_fp16 = const()[name = string("op_306_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_1_cast_fp16 = mul(x = var_305_cast_fp16, y = var_306_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor weights_1_cast_fp16 = softmax(axis = var_24, x = scores_1_cast_fp16)[name = string("weights_1_cast_fp16")]; + fp16 var_13_to_fp16 = const()[name = string("op_13_to_fp16"), val = fp16(0x1p+0)]; + string var_309_to_fp16_dtype_0 = const()[name = string("op_309_to_fp16_dtype_0"), val = string("fp16")]; + tensor src_pad_mask_to_fp16 = cast(dtype = var_309_to_fp16_dtype_0, x = src_pad_mask)[name = string("cast_2")]; + tensor var_310_cast_fp16 = sub(x = var_13_to_fp16, y = src_pad_mask_to_fp16)[name = string("op_310_cast_fp16")]; + tensor var_312_axes_0 = const()[name = string("op_312_axes_0"), val = tensor([1])]; + tensor var_312_cast_fp16 = expand_dims(axes = var_312_axes_0, x = var_310_cast_fp16)[name = string("op_312_cast_fp16")]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([2])]; + tensor var_313_cast_fp16 = expand_dims(axes = var_313_axes_0, x = var_312_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor weights_3_cast_fp16 = mul(x = weights_1_cast_fp16, y = var_313_cast_fp16)[name = string("weights_3_cast_fp16")]; + tensor var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor([-1])]; + bool var_317_keep_dims_0 = const()[name = string("op_317_keep_dims_0"), val = bool(true)]; + tensor var_317_cast_fp16 = reduce_sum(axes = var_317_axes_0, keep_dims = var_317_keep_dims_0, x = weights_3_cast_fp16)[name = string("op_317_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_318_cast_fp16 = maximum(x = var_317_cast_fp16, y = var_11_to_fp16)[name = string("op_318_cast_fp16")]; + tensor weights_5_cast_fp16 = real_div(x = weights_3_cast_fp16, y = var_318_cast_fp16)[name = string("weights_5_cast_fp16")]; + bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)]; + bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_302_cast_fp16)[name = string("transpose_77")]; + tensor context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = weights_5_cast_fp16, y = v_3_cast_fp16)[name = string("context_1_cast_fp16")]; + tensor var_321_perm_0 = const()[name = string("op_321_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 128, 640])]; + tensor var_321_cast_fp16 = transpose(perm = var_321_perm_0, x = context_1_cast_fp16)[name = string("transpose_76")]; + tensor context_3_cast_fp16 = reshape(shape = var_322, x = var_321_cast_fp16)[name = string("context_3_cast_fp16")]; + tensor module_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1945664))))[name = string("module_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1947008)))]; + tensor linear_1_cast_fp16 = linear(bias = module_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = context_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = input_1_cast_fp16, y = linear_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm1_weight_to_fp16 = const()[name = string("module_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1948352)))]; + tensor module_layers_0_norm1_bias_to_fp16 = const()[name = string("module_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1949696)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_5_cast_fp16 = layer_norm(axes = input_5_axes_0, beta = module_layers_0_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor module_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1951040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2770304))))[name = string("module_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_layers_0_linear1_bias_to_fp16 = const()[name = string("module_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2772928)))]; + tensor linear_2_cast_fp16 = linear(bias = module_layers_0_linear1_bias_to_fp16, weight = module_layers_0_linear1_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor input_9_cast_fp16 = relu(x = linear_2_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2775552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3594816))))[name = string("module_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_layers_0_linear2_bias_to_fp16 = const()[name = string("module_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3596160)))]; + tensor linear_3_cast_fp16 = linear(bias = module_layers_0_linear2_bias_to_fp16, weight = module_layers_0_linear2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_5_cast_fp16, y = linear_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm2_weight_to_fp16 = const()[name = string("module_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3597504)))]; + tensor module_layers_0_norm2_bias_to_fp16 = const()[name = string("module_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3598848)))]; + tensor x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = module_layers_0_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3600192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4829056))))[name = string("module_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4832960)))]; + tensor linear_4_cast_fp16 = linear(bias = module_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_345_split_sizes_0 = const()[name = string("op_345_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_345_axis_0 = const()[name = string("op_345_axis_0"), val = int32(-1)]; + tensor var_345_cast_fp16_0, tensor var_345_cast_fp16_1, tensor var_345_cast_fp16_2 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = linear_4_cast_fp16)[name = string("op_345_cast_fp16")]; + tensor var_348 = const()[name = string("op_348"), val = tensor([1, 128, 4, 160])]; + tensor var_349_cast_fp16 = reshape(shape = var_348, x = var_345_cast_fp16_0)[name = string("op_349_cast_fp16")]; + tensor var_351 = const()[name = string("op_351"), val = tensor([1, 128, 4, 160])]; + tensor var_352_cast_fp16 = reshape(shape = var_351, x = var_345_cast_fp16_1)[name = string("op_352_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 128, 4, 160])]; + tensor var_355_cast_fp16 = reshape(shape = var_354, x = var_345_cast_fp16_2)[name = string("op_355_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_358_transpose_x_0 = const()[name = string("op_358_transpose_x_0"), val = bool(false)]; + bool var_358_transpose_y_0 = const()[name = string("op_358_transpose_y_0"), val = bool(false)]; + tensor transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_352_cast_fp16)[name = string("transpose_74")]; + tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_349_cast_fp16)[name = string("transpose_75")]; + tensor var_358_cast_fp16 = matmul(transpose_x = var_358_transpose_x_0, transpose_y = var_358_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_3_cast_fp16 = mul(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("scores_3_cast_fp16")]; + tensor weights_7_cast_fp16 = softmax(axis = var_24, x = scores_3_cast_fp16)[name = string("weights_7_cast_fp16")]; + tensor weights_9_cast_fp16 = mul(x = weights_7_cast_fp16, y = var_313_cast_fp16)[name = string("weights_9_cast_fp16")]; + tensor var_370_axes_0 = const()[name = string("op_370_axes_0"), val = tensor([-1])]; + bool var_370_keep_dims_0 = const()[name = string("op_370_keep_dims_0"), val = bool(true)]; + tensor var_370_cast_fp16 = reduce_sum(axes = var_370_axes_0, keep_dims = var_370_keep_dims_0, x = weights_9_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_cast_fp16 = maximum(x = var_370_cast_fp16, y = var_11_to_fp16)[name = string("op_371_cast_fp16")]; + tensor weights_11_cast_fp16 = real_div(x = weights_9_cast_fp16, y = var_371_cast_fp16)[name = string("weights_11_cast_fp16")]; + bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)]; + bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_355_cast_fp16)[name = string("transpose_73")]; + tensor context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = weights_11_cast_fp16, y = v_7_cast_fp16)[name = string("context_5_cast_fp16")]; + tensor var_374_perm_0 = const()[name = string("op_374_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_375 = const()[name = string("op_375"), val = tensor([1, 128, 640])]; + tensor var_374_cast_fp16 = transpose(perm = var_374_perm_0, x = context_5_cast_fp16)[name = string("transpose_72")]; + tensor context_7_cast_fp16 = reshape(shape = var_375, x = var_374_cast_fp16)[name = string("context_7_cast_fp16")]; + tensor module_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4836864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5246528))))[name = string("module_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5247872)))]; + tensor linear_5_cast_fp16 = linear(bias = module_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = context_7_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_5_cast_fp16, y = linear_5_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm1_weight_to_fp16 = const()[name = string("module_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5249216)))]; + tensor module_layers_1_norm1_bias_to_fp16 = const()[name = string("module_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250560)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_layers_1_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5251904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6071168))))[name = string("module_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_layers_1_linear1_bias_to_fp16 = const()[name = string("module_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6073792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_layers_1_linear1_bias_to_fp16, weight = module_layers_1_linear1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = linear_6_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6076416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6895680))))[name = string("module_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_layers_1_linear2_bias_to_fp16 = const()[name = string("module_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6897024)))]; + tensor linear_7_cast_fp16 = linear(bias = module_layers_1_linear2_bias_to_fp16, weight = module_layers_1_linear2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = input_15_cast_fp16, y = linear_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm2_weight_to_fp16 = const()[name = string("module_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6898368)))]; + tensor module_layers_1_norm2_bias_to_fp16 = const()[name = string("module_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6899712)))]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = module_layers_1_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm2_weight_to_fp16, x = input_21_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor module_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8129920))))[name = string("module_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8133824)))]; + tensor linear_8_cast_fp16 = linear(bias = module_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = x_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_398_split_sizes_0 = const()[name = string("op_398_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_398_axis_0 = const()[name = string("op_398_axis_0"), val = int32(-1)]; + tensor var_398_cast_fp16_0, tensor var_398_cast_fp16_1, tensor var_398_cast_fp16_2 = split(axis = var_398_axis_0, split_sizes = var_398_split_sizes_0, x = linear_8_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_401 = const()[name = string("op_401"), val = tensor([1, 128, 4, 160])]; + tensor var_402_cast_fp16 = reshape(shape = var_401, x = var_398_cast_fp16_0)[name = string("op_402_cast_fp16")]; + tensor var_404 = const()[name = string("op_404"), val = tensor([1, 128, 4, 160])]; + tensor var_405_cast_fp16 = reshape(shape = var_404, x = var_398_cast_fp16_1)[name = string("op_405_cast_fp16")]; + tensor var_407 = const()[name = string("op_407"), val = tensor([1, 128, 4, 160])]; + tensor var_408_cast_fp16 = reshape(shape = var_407, x = var_398_cast_fp16_2)[name = string("op_408_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_411_transpose_x_0 = const()[name = string("op_411_transpose_x_0"), val = bool(false)]; + bool var_411_transpose_y_0 = const()[name = string("op_411_transpose_y_0"), val = bool(false)]; + tensor transpose_36_perm_0 = const()[name = string("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_37_perm_0 = const()[name = string("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_405_cast_fp16)[name = string("transpose_70")]; + tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_402_cast_fp16)[name = string("transpose_71")]; + tensor var_411_cast_fp16 = matmul(transpose_x = var_411_transpose_x_0, transpose_y = var_411_transpose_y_0, x = transpose_36, y = transpose_37)[name = string("op_411_cast_fp16")]; + fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_5_cast_fp16 = mul(x = var_411_cast_fp16, y = var_412_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor weights_13_cast_fp16 = softmax(axis = var_24, x = scores_5_cast_fp16)[name = string("weights_13_cast_fp16")]; + tensor weights_15_cast_fp16 = mul(x = weights_13_cast_fp16, y = var_313_cast_fp16)[name = string("weights_15_cast_fp16")]; + tensor var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor([-1])]; + bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)]; + tensor var_423_cast_fp16 = reduce_sum(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = weights_15_cast_fp16)[name = string("op_423_cast_fp16")]; + tensor var_424_cast_fp16 = maximum(x = var_423_cast_fp16, y = var_11_to_fp16)[name = string("op_424_cast_fp16")]; + tensor weights_17_cast_fp16 = real_div(x = weights_15_cast_fp16, y = var_424_cast_fp16)[name = string("weights_17_cast_fp16")]; + bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)]; + bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_408_cast_fp16)[name = string("transpose_69")]; + tensor context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = weights_17_cast_fp16, y = v_11_cast_fp16)[name = string("context_9_cast_fp16")]; + tensor var_427_perm_0 = const()[name = string("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 128, 640])]; + tensor var_427_cast_fp16 = transpose(perm = var_427_perm_0, x = context_9_cast_fp16)[name = string("transpose_68")]; + tensor context_11_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("context_11_cast_fp16")]; + tensor module_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8137728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8547392))))[name = string("module_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548736)))]; + tensor linear_9_cast_fp16 = linear(bias = module_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = context_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = x_7_cast_fp16, y = linear_9_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm1_weight_to_fp16 = const()[name = string("module_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550080)))]; + tensor module_layers_2_norm1_bias_to_fp16 = const()[name = string("module_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8551424)))]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = module_layers_2_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8552768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9372032))))[name = string("module_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_layers_2_linear1_bias_to_fp16 = const()[name = string("module_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9374656)))]; + tensor linear_10_cast_fp16 = linear(bias = module_layers_2_linear1_bias_to_fp16, weight = module_layers_2_linear1_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_29_cast_fp16 = relu(x = linear_10_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor module_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9377280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196544))))[name = string("module_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_layers_2_linear2_bias_to_fp16 = const()[name = string("module_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197888)))]; + tensor linear_11_cast_fp16 = linear(bias = module_layers_2_linear2_bias_to_fp16, weight = module_layers_2_linear2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_25_cast_fp16, y = linear_11_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm2_weight_to_fp16 = const()[name = string("module_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199232)))]; + tensor module_layers_2_norm2_bias_to_fp16 = const()[name = string("module_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10200576)))]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = module_layers_2_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor module_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10201920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11430784))))[name = string("module_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11434688)))]; + tensor linear_12_cast_fp16 = linear(bias = module_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = x_9_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(-1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1, tensor var_451_cast_fp16_2 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = linear_12_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_454 = const()[name = string("op_454"), val = tensor([1, 128, 4, 160])]; + tensor var_455_cast_fp16 = reshape(shape = var_454, x = var_451_cast_fp16_0)[name = string("op_455_cast_fp16")]; + tensor var_457 = const()[name = string("op_457"), val = tensor([1, 128, 4, 160])]; + tensor var_458_cast_fp16 = reshape(shape = var_457, x = var_451_cast_fp16_1)[name = string("op_458_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([1, 128, 4, 160])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = var_451_cast_fp16_2)[name = string("op_461_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_464_transpose_x_0 = const()[name = string("op_464_transpose_x_0"), val = bool(false)]; + bool var_464_transpose_y_0 = const()[name = string("op_464_transpose_y_0"), val = bool(false)]; + tensor transpose_38_perm_0 = const()[name = string("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_39_perm_0 = const()[name = string("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_458_cast_fp16)[name = string("transpose_66")]; + tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_455_cast_fp16)[name = string("transpose_67")]; + tensor var_464_cast_fp16 = matmul(transpose_x = var_464_transpose_x_0, transpose_y = var_464_transpose_y_0, x = transpose_38, y = transpose_39)[name = string("op_464_cast_fp16")]; + fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_7_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("scores_7_cast_fp16")]; + tensor weights_19_cast_fp16 = softmax(axis = var_24, x = scores_7_cast_fp16)[name = string("weights_19_cast_fp16")]; + tensor weights_21_cast_fp16 = mul(x = weights_19_cast_fp16, y = var_313_cast_fp16)[name = string("weights_21_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + bool var_476_keep_dims_0 = const()[name = string("op_476_keep_dims_0"), val = bool(true)]; + tensor var_476_cast_fp16 = reduce_sum(axes = var_476_axes_0, keep_dims = var_476_keep_dims_0, x = weights_21_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor var_477_cast_fp16 = maximum(x = var_476_cast_fp16, y = var_11_to_fp16)[name = string("op_477_cast_fp16")]; + tensor weights_23_cast_fp16 = real_div(x = weights_21_cast_fp16, y = var_477_cast_fp16)[name = string("weights_23_cast_fp16")]; + bool context_13_transpose_x_0 = const()[name = string("context_13_transpose_x_0"), val = bool(false)]; + bool context_13_transpose_y_0 = const()[name = string("context_13_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_461_cast_fp16)[name = string("transpose_65")]; + tensor context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = weights_23_cast_fp16, y = v_15_cast_fp16)[name = string("context_13_cast_fp16")]; + tensor var_480_perm_0 = const()[name = string("op_480_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_481 = const()[name = string("op_481"), val = tensor([1, 128, 640])]; + tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = context_13_cast_fp16)[name = string("transpose_64")]; + tensor context_15_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("context_15_cast_fp16")]; + tensor module_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11438592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11848256))))[name = string("module_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11849600)))]; + tensor linear_13_cast_fp16 = linear(bias = module_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = context_15_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = x_9_cast_fp16, y = linear_13_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_axes_0 = const()[name = string("input_35_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm1_weight_to_fp16 = const()[name = string("module_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11850944)))]; + tensor module_layers_3_norm1_bias_to_fp16 = const()[name = string("module_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11852288)))]; + tensor input_35_cast_fp16 = layer_norm(axes = input_35_axes_0, beta = module_layers_3_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11853632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12672896))))[name = string("module_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_layers_3_linear1_bias_to_fp16 = const()[name = string("module_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12675520)))]; + tensor linear_14_cast_fp16 = linear(bias = module_layers_3_linear1_bias_to_fp16, weight = module_layers_3_linear1_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor input_39_cast_fp16 = relu(x = linear_14_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12678144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13497408))))[name = string("module_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_layers_3_linear2_bias_to_fp16 = const()[name = string("module_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13498752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_layers_3_linear2_bias_to_fp16, weight = module_layers_3_linear2_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm2_weight_to_fp16 = const()[name = string("module_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13500096)))]; + tensor module_layers_3_norm2_bias_to_fp16 = const()[name = string("module_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13501440)))]; + tensor x_11_cast_fp16 = layer_norm(axes = x_11_axes_0, beta = module_layers_3_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm2_weight_to_fp16, x = input_41_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13502784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14731648))))[name = string("module_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14735552)))]; + tensor linear_16_cast_fp16 = linear(bias = module_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_504_split_sizes_0 = const()[name = string("op_504_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_504_axis_0 = const()[name = string("op_504_axis_0"), val = int32(-1)]; + tensor var_504_cast_fp16_0, tensor var_504_cast_fp16_1, tensor var_504_cast_fp16_2 = split(axis = var_504_axis_0, split_sizes = var_504_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_504_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 128, 4, 160])]; + tensor var_508_cast_fp16 = reshape(shape = var_507, x = var_504_cast_fp16_0)[name = string("op_508_cast_fp16")]; + tensor var_510 = const()[name = string("op_510"), val = tensor([1, 128, 4, 160])]; + tensor var_511_cast_fp16 = reshape(shape = var_510, x = var_504_cast_fp16_1)[name = string("op_511_cast_fp16")]; + tensor var_513 = const()[name = string("op_513"), val = tensor([1, 128, 4, 160])]; + tensor var_514_cast_fp16 = reshape(shape = var_513, x = var_504_cast_fp16_2)[name = string("op_514_cast_fp16")]; + tensor v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_517_transpose_x_0 = const()[name = string("op_517_transpose_x_0"), val = bool(false)]; + bool var_517_transpose_y_0 = const()[name = string("op_517_transpose_y_0"), val = bool(false)]; + tensor transpose_40_perm_0 = const()[name = string("transpose_40_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_41_perm_0 = const()[name = string("transpose_41_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_41 = transpose(perm = transpose_41_perm_0, x = var_511_cast_fp16)[name = string("transpose_62")]; + tensor transpose_40 = transpose(perm = transpose_40_perm_0, x = var_508_cast_fp16)[name = string("transpose_63")]; + tensor var_517_cast_fp16 = matmul(transpose_x = var_517_transpose_x_0, transpose_y = var_517_transpose_y_0, x = transpose_40, y = transpose_41)[name = string("op_517_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_9_cast_fp16 = mul(x = var_517_cast_fp16, y = var_518_to_fp16)[name = string("scores_9_cast_fp16")]; + tensor weights_25_cast_fp16 = softmax(axis = var_24, x = scores_9_cast_fp16)[name = string("weights_25_cast_fp16")]; + tensor weights_27_cast_fp16 = mul(x = weights_25_cast_fp16, y = var_313_cast_fp16)[name = string("weights_27_cast_fp16")]; + tensor var_529_axes_0 = const()[name = string("op_529_axes_0"), val = tensor([-1])]; + bool var_529_keep_dims_0 = const()[name = string("op_529_keep_dims_0"), val = bool(true)]; + tensor var_529_cast_fp16 = reduce_sum(axes = var_529_axes_0, keep_dims = var_529_keep_dims_0, x = weights_27_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = maximum(x = var_529_cast_fp16, y = var_11_to_fp16)[name = string("op_530_cast_fp16")]; + tensor weights_29_cast_fp16 = real_div(x = weights_27_cast_fp16, y = var_530_cast_fp16)[name = string("weights_29_cast_fp16")]; + bool context_17_transpose_x_0 = const()[name = string("context_17_transpose_x_0"), val = bool(false)]; + bool context_17_transpose_y_0 = const()[name = string("context_17_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = v_19_perm_0, x = var_514_cast_fp16)[name = string("transpose_61")]; + tensor context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = weights_29_cast_fp16, y = v_19_cast_fp16)[name = string("context_17_cast_fp16")]; + tensor var_533_perm_0 = const()[name = string("op_533_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 128, 640])]; + tensor var_533_cast_fp16 = transpose(perm = var_533_perm_0, x = context_17_cast_fp16)[name = string("transpose_60")]; + tensor context_19_cast_fp16 = reshape(shape = var_534, x = var_533_cast_fp16)[name = string("context_19_cast_fp16")]; + tensor module_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14739456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15149120))))[name = string("module_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15150464)))]; + tensor linear_17_cast_fp16 = linear(bias = module_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = context_19_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = x_11_cast_fp16, y = linear_17_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm1_weight_to_fp16 = const()[name = string("module_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15151808)))]; + tensor module_layers_4_norm1_bias_to_fp16 = const()[name = string("module_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15153152)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = module_layers_4_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15973760))))[name = string("module_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_layers_4_linear1_bias_to_fp16 = const()[name = string("module_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15976384)))]; + tensor linear_18_cast_fp16 = linear(bias = module_layers_4_linear1_bias_to_fp16, weight = module_layers_4_linear1_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = linear_18_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16798272))))[name = string("module_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_layers_4_linear2_bias_to_fp16 = const()[name = string("module_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16799616)))]; + tensor linear_19_cast_fp16 = linear(bias = module_layers_4_linear2_bias_to_fp16, weight = module_layers_4_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_45_cast_fp16, y = linear_19_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm2_weight_to_fp16 = const()[name = string("module_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16800960)))]; + tensor module_layers_4_norm2_bias_to_fp16 = const()[name = string("module_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16802304)))]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = module_layers_4_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm2_weight_to_fp16, x = input_51_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor module_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16803648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18032512))))[name = string("module_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor linear_20_cast_fp16 = linear(bias = module_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = x_13_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_557_split_sizes_0 = const()[name = string("op_557_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_557_axis_0 = const()[name = string("op_557_axis_0"), val = int32(-1)]; + tensor var_557_cast_fp16_0, tensor var_557_cast_fp16_1, tensor var_557_cast_fp16_2 = split(axis = var_557_axis_0, split_sizes = var_557_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_560 = const()[name = string("op_560"), val = tensor([1, 128, 4, 160])]; + tensor var_561_cast_fp16 = reshape(shape = var_560, x = var_557_cast_fp16_0)[name = string("op_561_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([1, 128, 4, 160])]; + tensor var_564_cast_fp16 = reshape(shape = var_563, x = var_557_cast_fp16_1)[name = string("op_564_cast_fp16")]; + tensor var_566 = const()[name = string("op_566"), val = tensor([1, 128, 4, 160])]; + tensor var_567_cast_fp16 = reshape(shape = var_566, x = var_557_cast_fp16_2)[name = string("op_567_cast_fp16")]; + tensor v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_570_transpose_x_0 = const()[name = string("op_570_transpose_x_0"), val = bool(false)]; + bool var_570_transpose_y_0 = const()[name = string("op_570_transpose_y_0"), val = bool(false)]; + tensor transpose_42_perm_0 = const()[name = string("transpose_42_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_43_perm_0 = const()[name = string("transpose_43_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_43 = transpose(perm = transpose_43_perm_0, x = var_564_cast_fp16)[name = string("transpose_58")]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = var_561_cast_fp16)[name = string("transpose_59")]; + tensor var_570_cast_fp16 = matmul(transpose_x = var_570_transpose_x_0, transpose_y = var_570_transpose_y_0, x = transpose_42, y = transpose_43)[name = string("op_570_cast_fp16")]; + fp16 var_571_to_fp16 = const()[name = string("op_571_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_11_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = string("scores_11_cast_fp16")]; + tensor weights_31_cast_fp16 = softmax(axis = var_24, x = scores_11_cast_fp16)[name = string("weights_31_cast_fp16")]; + tensor weights_33_cast_fp16 = mul(x = weights_31_cast_fp16, y = var_313_cast_fp16)[name = string("weights_33_cast_fp16")]; + tensor var_582_axes_0 = const()[name = string("op_582_axes_0"), val = tensor([-1])]; + bool var_582_keep_dims_0 = const()[name = string("op_582_keep_dims_0"), val = bool(true)]; + tensor var_582_cast_fp16 = reduce_sum(axes = var_582_axes_0, keep_dims = var_582_keep_dims_0, x = weights_33_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = maximum(x = var_582_cast_fp16, y = var_11_to_fp16)[name = string("op_583_cast_fp16")]; + tensor weights_35_cast_fp16 = real_div(x = weights_33_cast_fp16, y = var_583_cast_fp16)[name = string("weights_35_cast_fp16")]; + bool context_21_transpose_x_0 = const()[name = string("context_21_transpose_x_0"), val = bool(false)]; + bool context_21_transpose_y_0 = const()[name = string("context_21_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = v_23_perm_0, x = var_567_cast_fp16)[name = string("transpose_57")]; + tensor context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = weights_35_cast_fp16, y = v_23_cast_fp16)[name = string("context_21_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 128, 640])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = context_21_cast_fp16)[name = string("transpose_56")]; + tensor context_23_cast_fp16 = reshape(shape = var_587, x = var_586_cast_fp16)[name = string("context_23_cast_fp16")]; + tensor module_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18040320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18449984))))[name = string("module_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18451328)))]; + tensor linear_21_cast_fp16 = linear(bias = module_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = context_23_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = x_13_cast_fp16, y = linear_21_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm1_weight_to_fp16 = const()[name = string("module_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18452672)))]; + tensor module_layers_5_norm1_bias_to_fp16 = const()[name = string("module_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18454016)))]; + tensor input_55_cast_fp16 = layer_norm(axes = input_55_axes_0, beta = module_layers_5_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm1_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18455360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19274624))))[name = string("module_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_layers_5_linear1_bias_to_fp16 = const()[name = string("module_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19277248)))]; + tensor linear_22_cast_fp16 = linear(bias = module_layers_5_linear1_bias_to_fp16, weight = module_layers_5_linear1_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_59_cast_fp16 = relu(x = linear_22_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19279872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20099136))))[name = string("module_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_layers_5_linear2_bias_to_fp16 = const()[name = string("module_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20100480)))]; + tensor linear_23_cast_fp16 = linear(bias = module_layers_5_linear2_bias_to_fp16, weight = module_layers_5_linear2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_55_cast_fp16, y = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm2_weight_to_fp16 = const()[name = string("module_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20101824)))]; + tensor module_layers_5_norm2_bias_to_fp16 = const()[name = string("module_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20103168)))]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, beta = module_layers_5_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor module_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21333376))))[name = string("module_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21337280)))]; + tensor linear_24_cast_fp16 = linear(bias = module_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = x_15_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_610_split_sizes_0 = const()[name = string("op_610_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_610_axis_0 = const()[name = string("op_610_axis_0"), val = int32(-1)]; + tensor var_610_cast_fp16_0, tensor var_610_cast_fp16_1, tensor var_610_cast_fp16_2 = split(axis = var_610_axis_0, split_sizes = var_610_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor var_613 = const()[name = string("op_613"), val = tensor([1, 128, 4, 160])]; + tensor var_614_cast_fp16 = reshape(shape = var_613, x = var_610_cast_fp16_0)[name = string("op_614_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 128, 4, 160])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_610_cast_fp16_1)[name = string("op_617_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 128, 4, 160])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = var_610_cast_fp16_2)[name = string("op_620_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_623_transpose_x_0 = const()[name = string("op_623_transpose_x_0"), val = bool(false)]; + bool var_623_transpose_y_0 = const()[name = string("op_623_transpose_y_0"), val = bool(false)]; + tensor transpose_44_perm_0 = const()[name = string("transpose_44_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_45_perm_0 = const()[name = string("transpose_45_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_45 = transpose(perm = transpose_45_perm_0, x = var_617_cast_fp16)[name = string("transpose_54")]; + tensor transpose_44 = transpose(perm = transpose_44_perm_0, x = var_614_cast_fp16)[name = string("transpose_55")]; + tensor var_623_cast_fp16 = matmul(transpose_x = var_623_transpose_x_0, transpose_y = var_623_transpose_y_0, x = transpose_44, y = transpose_45)[name = string("op_623_cast_fp16")]; + fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_13_cast_fp16 = mul(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("scores_13_cast_fp16")]; + tensor weights_37_cast_fp16 = softmax(axis = var_24, x = scores_13_cast_fp16)[name = string("weights_37_cast_fp16")]; + tensor weights_39_cast_fp16 = mul(x = weights_37_cast_fp16, y = var_313_cast_fp16)[name = string("weights_39_cast_fp16")]; + tensor var_635_axes_0 = const()[name = string("op_635_axes_0"), val = tensor([-1])]; + bool var_635_keep_dims_0 = const()[name = string("op_635_keep_dims_0"), val = bool(true)]; + tensor var_635_cast_fp16 = reduce_sum(axes = var_635_axes_0, keep_dims = var_635_keep_dims_0, x = weights_39_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_cast_fp16 = maximum(x = var_635_cast_fp16, y = var_11_to_fp16)[name = string("op_636_cast_fp16")]; + tensor weights_41_cast_fp16 = real_div(x = weights_39_cast_fp16, y = var_636_cast_fp16)[name = string("weights_41_cast_fp16")]; + bool context_25_transpose_x_0 = const()[name = string("context_25_transpose_x_0"), val = bool(false)]; + bool context_25_transpose_y_0 = const()[name = string("context_25_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_620_cast_fp16)[name = string("transpose_53")]; + tensor context_25_cast_fp16 = matmul(transpose_x = context_25_transpose_x_0, transpose_y = context_25_transpose_y_0, x = weights_41_cast_fp16, y = v_27_cast_fp16)[name = string("context_25_cast_fp16")]; + tensor var_639_perm_0 = const()[name = string("op_639_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_640 = const()[name = string("op_640"), val = tensor([1, 128, 640])]; + tensor var_639_cast_fp16 = transpose(perm = var_639_perm_0, x = context_25_cast_fp16)[name = string("transpose_52")]; + tensor context_27_cast_fp16 = reshape(shape = var_640, x = var_639_cast_fp16)[name = string("context_27_cast_fp16")]; + tensor module_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21341184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750848))))[name = string("module_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21752192)))]; + tensor linear_25_cast_fp16 = linear(bias = module_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = context_27_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = x_15_cast_fp16, y = linear_25_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm1_weight_to_fp16 = const()[name = string("module_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21753536)))]; + tensor module_layers_6_norm1_bias_to_fp16 = const()[name = string("module_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21754880)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = module_layers_6_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21756224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22575488))))[name = string("module_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_layers_6_linear1_bias_to_fp16 = const()[name = string("module_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22578112)))]; + tensor linear_26_cast_fp16 = linear(bias = module_layers_6_linear1_bias_to_fp16, weight = module_layers_6_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = linear_26_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22580736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23400000))))[name = string("module_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_layers_6_linear2_bias_to_fp16 = const()[name = string("module_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23401344)))]; + tensor linear_27_cast_fp16 = linear(bias = module_layers_6_linear2_bias_to_fp16, weight = module_layers_6_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = input_65_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm2_weight_to_fp16 = const()[name = string("module_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23402688)))]; + tensor module_layers_6_norm2_bias_to_fp16 = const()[name = string("module_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404032)))]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, beta = module_layers_6_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm2_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_cast_fp16")]; + tensor module_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23405376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24634240))))[name = string("module_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24638144)))]; + tensor linear_28_cast_fp16 = linear(bias = module_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = x_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_663_split_sizes_0 = const()[name = string("op_663_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_663_axis_0 = const()[name = string("op_663_axis_0"), val = int32(-1)]; + tensor var_663_cast_fp16_0, tensor var_663_cast_fp16_1, tensor var_663_cast_fp16_2 = split(axis = var_663_axis_0, split_sizes = var_663_split_sizes_0, x = linear_28_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_666 = const()[name = string("op_666"), val = tensor([1, 128, 4, 160])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = var_663_cast_fp16_0)[name = string("op_667_cast_fp16")]; + tensor var_669 = const()[name = string("op_669"), val = tensor([1, 128, 4, 160])]; + tensor var_670_cast_fp16 = reshape(shape = var_669, x = var_663_cast_fp16_1)[name = string("op_670_cast_fp16")]; + tensor var_672 = const()[name = string("op_672"), val = tensor([1, 128, 4, 160])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = var_663_cast_fp16_2)[name = string("op_673_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_676_transpose_x_0 = const()[name = string("op_676_transpose_x_0"), val = bool(false)]; + bool var_676_transpose_y_0 = const()[name = string("op_676_transpose_y_0"), val = bool(false)]; + tensor transpose_46_perm_0 = const()[name = string("transpose_46_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_47_perm_0 = const()[name = string("transpose_47_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_47 = transpose(perm = transpose_47_perm_0, x = var_670_cast_fp16)[name = string("transpose_50")]; + tensor transpose_46 = transpose(perm = transpose_46_perm_0, x = var_667_cast_fp16)[name = string("transpose_51")]; + tensor var_676_cast_fp16 = matmul(transpose_x = var_676_transpose_x_0, transpose_y = var_676_transpose_y_0, x = transpose_46, y = transpose_47)[name = string("op_676_cast_fp16")]; + fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_cast_fp16 = mul(x = var_676_cast_fp16, y = var_677_to_fp16)[name = string("scores_cast_fp16")]; + tensor weights_43_cast_fp16 = softmax(axis = var_24, x = scores_cast_fp16)[name = string("weights_43_cast_fp16")]; + tensor weights_45_cast_fp16 = mul(x = weights_43_cast_fp16, y = var_313_cast_fp16)[name = string("weights_45_cast_fp16")]; + tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([-1])]; + bool var_688_keep_dims_0 = const()[name = string("op_688_keep_dims_0"), val = bool(true)]; + tensor var_688_cast_fp16 = reduce_sum(axes = var_688_axes_0, keep_dims = var_688_keep_dims_0, x = weights_45_cast_fp16)[name = string("op_688_cast_fp16")]; + tensor var_689_cast_fp16 = maximum(x = var_688_cast_fp16, y = var_11_to_fp16)[name = string("op_689_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_45_cast_fp16, y = var_689_cast_fp16)[name = string("weights_cast_fp16")]; + bool context_29_transpose_x_0 = const()[name = string("context_29_transpose_x_0"), val = bool(false)]; + bool context_29_transpose_y_0 = const()[name = string("context_29_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_673_cast_fp16)[name = string("transpose_49")]; + tensor context_29_cast_fp16 = matmul(transpose_x = context_29_transpose_x_0, transpose_y = context_29_transpose_y_0, x = weights_cast_fp16, y = v_cast_fp16)[name = string("context_29_cast_fp16")]; + tensor var_692_perm_0 = const()[name = string("op_692_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_693 = const()[name = string("op_693"), val = tensor([1, 128, 640])]; + tensor var_692_cast_fp16 = transpose(perm = var_692_perm_0, x = context_29_cast_fp16)[name = string("transpose_48")]; + tensor context_cast_fp16 = reshape(shape = var_693, x = var_692_cast_fp16)[name = string("context_cast_fp16")]; + tensor module_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24642048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25051712))))[name = string("module_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25053056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = context_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = x_cast_fp16, y = linear_29_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm1_weight_to_fp16 = const()[name = string("module_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25054400)))]; + tensor module_layers_7_norm1_bias_to_fp16 = const()[name = string("module_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25055744)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_layers_7_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm1_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25057088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25876352))))[name = string("module_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_layers_7_linear1_bias_to_fp16 = const()[name = string("module_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25878976)))]; + tensor linear_30_cast_fp16 = linear(bias = module_layers_7_linear1_bias_to_fp16, weight = module_layers_7_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_79_cast_fp16 = relu(x = linear_30_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25881600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26700864))))[name = string("module_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_layers_7_linear2_bias_to_fp16 = const()[name = string("module_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26702208)))]; + tensor linear_31_cast_fp16 = linear(bias = module_layers_7_linear2_bias_to_fp16, weight = module_layers_7_linear2_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = input_75_cast_fp16, y = linear_31_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor input_83_axes_0 = const()[name = string("input_83_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm2_weight_to_fp16 = const()[name = string("module_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26703552)))]; + tensor module_layers_7_norm2_bias_to_fp16 = const()[name = string("module_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26704896)))]; + tensor input_83_cast_fp16 = layer_norm(axes = input_83_axes_0, beta = module_layers_7_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm2_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([-1])]; + tensor module_encoder_norm_weight_to_fp16 = const()[name = string("module_encoder_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26706240)))]; + tensor module_encoder_norm_bias_to_fp16 = const()[name = string("module_encoder_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26707584)))]; + tensor input_cast_fp16 = layer_norm(axes = input_axes_0, beta = module_encoder_norm_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_encoder_norm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_cast_fp16")]; + tensor module_fc_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26708928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770432))))[name = string("module_fc_out_weight_to_fp16_quantized")]; + tensor module_fc_out_bias_to_fp16 = const()[name = string("module_fc_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770688)))]; + tensor linear_32_cast_fp16 = linear(bias = module_fc_out_bias_to_fp16, weight = module_fc_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor padded_axes_0 = const()[name = string("padded_axes_0"), val = tensor([-1])]; + tensor padded = expand_dims(axes = padded_axes_0, x = src_pad_mask)[name = string("padded")]; + tensor var_723_begin_0 = const()[name = string("op_723_begin_0"), val = tensor([0, 0, 0])]; + tensor var_723_end_0 = const()[name = string("op_723_end_0"), val = tensor([1, 128, 1])]; + tensor var_723_end_mask_0 = const()[name = string("op_723_end_mask_0"), val = tensor([true, true, false])]; + tensor var_723_cast_fp16 = slice_by_index(begin = var_723_begin_0, end = var_723_end_0, end_mask = var_723_end_mask_0, x = linear_32_cast_fp16)[name = string("op_723_cast_fp16")]; + tensor var_722_to_fp16 = const()[name = string("op_722_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26853120)))]; + tensor blank_logits_cast_fp16 = select(a = var_722_to_fp16, b = var_723_cast_fp16, cond = padded)[name = string("blank_logits_cast_fp16")]; + tensor var_725_begin_0 = const()[name = string("op_725_begin_0"), val = tensor([0, 0, 1])]; + tensor var_725_end_0 = const()[name = string("op_725_end_0"), val = tensor([1, 128, 96])]; + tensor var_725_end_mask_0 = const()[name = string("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725_cast_fp16 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = linear_32_cast_fp16)[name = string("op_725_cast_fp16")]; + fp16 var_726_value_0_to_fp16 = const()[name = string("op_726_value_0_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor var_726_cast_fp16 = fill_like(ref_tensor = var_725_cast_fp16, value = var_726_value_0_to_fp16)[name = string("op_726_cast_fp16")]; + tensor padded_after_broadcast_reps_0 = const()[name = string("padded_after_broadcast_reps_0"), val = tensor([1, 1, 95])]; + tensor padded_after_broadcast = tile(reps = padded_after_broadcast_reps_0, x = padded)[name = string("padded_after_broadcast")]; + tensor nonblank_logits_cast_fp16 = select(a = var_726_cast_fp16, b = var_725_cast_fp16, cond = padded_after_broadcast)[name = string("nonblank_logits_cast_fp16")]; + bool var_730_interleave_0 = const()[name = string("op_730_interleave_0"), val = bool(false)]; + tensor var_730_cast_fp16 = concat(axis = var_24, interleave = var_730_interleave_0, values = (blank_logits_cast_fp16, nonblank_logits_cast_fp16))[name = string("op_730_cast_fp16")]; + string var_730_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_730_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_730_cast_fp16_to_fp32_dtype_0, x = var_730_cast_fp16)[name = string("cast_1")]; + } -> (logits); + func g2p_256(tensor text) { + int32 var_24 = const()[name = string("op_24"), val = int32(-1)]; + int32 var_26 = const()[name = string("op_26"), val = int32(0)]; + tensor src_pad_mask = equal(x = text, y = var_26)[name = string("src_pad_mask")]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor module_embedding_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257408))))[name = string("module_embedding_weight_to_fp16_quantized")]; + string text_to_int16_dtype_0 = const()[name = string("text_to_int16_dtype_0"), val = string("int16")]; + string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_to_int16 = cast(dtype = text_to_int16_dtype_0, x = text)[name = string("cast_6")]; + tensor cast_49 = cast(dtype = cast_49_dtype_0, x = text_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_49, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(402)]; + tensor add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_3")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(402)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 x_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_1, validate_indices = x_1_validate_indices_0, x = module_embedding_weight_to_fp16_quantized)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor op_286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26853440))), scale = tensor([[[0x1.724p-7]]]))[name = string("op_286_to_fp16_quantized")]; + tensor input_1_cast_fp16 = add(x = x_1_cast_fp16_cast_uint16_cast_uint16, y = op_286_to_fp16_quantized)[name = string("input_1_cast_fp16")]; + tensor module_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528192))))[name = string("module_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1532096)))]; + tensor linear_0_cast_fp16 = linear(bias = module_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_292_split_sizes_0 = const()[name = string("op_292_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_292_axis_0 = const()[name = string("op_292_axis_0"), val = int32(-1)]; + tensor var_292_cast_fp16_0, tensor var_292_cast_fp16_1, tensor var_292_cast_fp16_2 = split(axis = var_292_axis_0, split_sizes = var_292_split_sizes_0, x = linear_0_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor var_295 = const()[name = string("op_295"), val = tensor([1, 256, 4, 160])]; + tensor var_296_cast_fp16 = reshape(shape = var_295, x = var_292_cast_fp16_0)[name = string("op_296_cast_fp16")]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 256, 4, 160])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = var_292_cast_fp16_1)[name = string("op_299_cast_fp16")]; + tensor var_301 = const()[name = string("op_301"), val = tensor([1, 256, 4, 160])]; + tensor var_302_cast_fp16 = reshape(shape = var_301, x = var_292_cast_fp16_2)[name = string("op_302_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_305_transpose_x_0 = const()[name = string("op_305_transpose_x_0"), val = bool(false)]; + bool var_305_transpose_y_0 = const()[name = string("op_305_transpose_y_0"), val = bool(false)]; + tensor transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_299_cast_fp16)[name = string("transpose_78")]; + tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_296_cast_fp16)[name = string("transpose_79")]; + tensor var_305_cast_fp16 = matmul(transpose_x = var_305_transpose_x_0, transpose_y = var_305_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_305_cast_fp16")]; + fp16 var_306_to_fp16 = const()[name = string("op_306_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_1_cast_fp16 = mul(x = var_305_cast_fp16, y = var_306_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor weights_1_cast_fp16 = softmax(axis = var_24, x = scores_1_cast_fp16)[name = string("weights_1_cast_fp16")]; + fp16 var_13_to_fp16 = const()[name = string("op_13_to_fp16"), val = fp16(0x1p+0)]; + string var_309_to_fp16_dtype_0 = const()[name = string("op_309_to_fp16_dtype_0"), val = string("fp16")]; + tensor src_pad_mask_to_fp16 = cast(dtype = var_309_to_fp16_dtype_0, x = src_pad_mask)[name = string("cast_2")]; + tensor var_310_cast_fp16 = sub(x = var_13_to_fp16, y = src_pad_mask_to_fp16)[name = string("op_310_cast_fp16")]; + tensor var_312_axes_0 = const()[name = string("op_312_axes_0"), val = tensor([1])]; + tensor var_312_cast_fp16 = expand_dims(axes = var_312_axes_0, x = var_310_cast_fp16)[name = string("op_312_cast_fp16")]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([2])]; + tensor var_313_cast_fp16 = expand_dims(axes = var_313_axes_0, x = var_312_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor weights_3_cast_fp16 = mul(x = weights_1_cast_fp16, y = var_313_cast_fp16)[name = string("weights_3_cast_fp16")]; + tensor var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor([-1])]; + bool var_317_keep_dims_0 = const()[name = string("op_317_keep_dims_0"), val = bool(true)]; + tensor var_317_cast_fp16 = reduce_sum(axes = var_317_axes_0, keep_dims = var_317_keep_dims_0, x = weights_3_cast_fp16)[name = string("op_317_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_318_cast_fp16 = maximum(x = var_317_cast_fp16, y = var_11_to_fp16)[name = string("op_318_cast_fp16")]; + tensor weights_5_cast_fp16 = real_div(x = weights_3_cast_fp16, y = var_318_cast_fp16)[name = string("weights_5_cast_fp16")]; + bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)]; + bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_302_cast_fp16)[name = string("transpose_77")]; + tensor context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = weights_5_cast_fp16, y = v_3_cast_fp16)[name = string("context_1_cast_fp16")]; + tensor var_321_perm_0 = const()[name = string("op_321_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 256, 640])]; + tensor var_321_cast_fp16 = transpose(perm = var_321_perm_0, x = context_1_cast_fp16)[name = string("transpose_76")]; + tensor context_3_cast_fp16 = reshape(shape = var_322, x = var_321_cast_fp16)[name = string("context_3_cast_fp16")]; + tensor module_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1945664))))[name = string("module_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1947008)))]; + tensor linear_1_cast_fp16 = linear(bias = module_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = context_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = input_1_cast_fp16, y = linear_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm1_weight_to_fp16 = const()[name = string("module_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1948352)))]; + tensor module_layers_0_norm1_bias_to_fp16 = const()[name = string("module_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1949696)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_5_cast_fp16 = layer_norm(axes = input_5_axes_0, beta = module_layers_0_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor module_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1951040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2770304))))[name = string("module_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_layers_0_linear1_bias_to_fp16 = const()[name = string("module_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2772928)))]; + tensor linear_2_cast_fp16 = linear(bias = module_layers_0_linear1_bias_to_fp16, weight = module_layers_0_linear1_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor input_9_cast_fp16 = relu(x = linear_2_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2775552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3594816))))[name = string("module_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_layers_0_linear2_bias_to_fp16 = const()[name = string("module_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3596160)))]; + tensor linear_3_cast_fp16 = linear(bias = module_layers_0_linear2_bias_to_fp16, weight = module_layers_0_linear2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_5_cast_fp16, y = linear_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm2_weight_to_fp16 = const()[name = string("module_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3597504)))]; + tensor module_layers_0_norm2_bias_to_fp16 = const()[name = string("module_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3598848)))]; + tensor x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = module_layers_0_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3600192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4829056))))[name = string("module_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4832960)))]; + tensor linear_4_cast_fp16 = linear(bias = module_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_345_split_sizes_0 = const()[name = string("op_345_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_345_axis_0 = const()[name = string("op_345_axis_0"), val = int32(-1)]; + tensor var_345_cast_fp16_0, tensor var_345_cast_fp16_1, tensor var_345_cast_fp16_2 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = linear_4_cast_fp16)[name = string("op_345_cast_fp16")]; + tensor var_348 = const()[name = string("op_348"), val = tensor([1, 256, 4, 160])]; + tensor var_349_cast_fp16 = reshape(shape = var_348, x = var_345_cast_fp16_0)[name = string("op_349_cast_fp16")]; + tensor var_351 = const()[name = string("op_351"), val = tensor([1, 256, 4, 160])]; + tensor var_352_cast_fp16 = reshape(shape = var_351, x = var_345_cast_fp16_1)[name = string("op_352_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 256, 4, 160])]; + tensor var_355_cast_fp16 = reshape(shape = var_354, x = var_345_cast_fp16_2)[name = string("op_355_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_358_transpose_x_0 = const()[name = string("op_358_transpose_x_0"), val = bool(false)]; + bool var_358_transpose_y_0 = const()[name = string("op_358_transpose_y_0"), val = bool(false)]; + tensor transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_352_cast_fp16)[name = string("transpose_74")]; + tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_349_cast_fp16)[name = string("transpose_75")]; + tensor var_358_cast_fp16 = matmul(transpose_x = var_358_transpose_x_0, transpose_y = var_358_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_3_cast_fp16 = mul(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("scores_3_cast_fp16")]; + tensor weights_7_cast_fp16 = softmax(axis = var_24, x = scores_3_cast_fp16)[name = string("weights_7_cast_fp16")]; + tensor weights_9_cast_fp16 = mul(x = weights_7_cast_fp16, y = var_313_cast_fp16)[name = string("weights_9_cast_fp16")]; + tensor var_370_axes_0 = const()[name = string("op_370_axes_0"), val = tensor([-1])]; + bool var_370_keep_dims_0 = const()[name = string("op_370_keep_dims_0"), val = bool(true)]; + tensor var_370_cast_fp16 = reduce_sum(axes = var_370_axes_0, keep_dims = var_370_keep_dims_0, x = weights_9_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_cast_fp16 = maximum(x = var_370_cast_fp16, y = var_11_to_fp16)[name = string("op_371_cast_fp16")]; + tensor weights_11_cast_fp16 = real_div(x = weights_9_cast_fp16, y = var_371_cast_fp16)[name = string("weights_11_cast_fp16")]; + bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)]; + bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_355_cast_fp16)[name = string("transpose_73")]; + tensor context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = weights_11_cast_fp16, y = v_7_cast_fp16)[name = string("context_5_cast_fp16")]; + tensor var_374_perm_0 = const()[name = string("op_374_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_375 = const()[name = string("op_375"), val = tensor([1, 256, 640])]; + tensor var_374_cast_fp16 = transpose(perm = var_374_perm_0, x = context_5_cast_fp16)[name = string("transpose_72")]; + tensor context_7_cast_fp16 = reshape(shape = var_375, x = var_374_cast_fp16)[name = string("context_7_cast_fp16")]; + tensor module_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4836864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5246528))))[name = string("module_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5247872)))]; + tensor linear_5_cast_fp16 = linear(bias = module_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = context_7_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_5_cast_fp16, y = linear_5_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm1_weight_to_fp16 = const()[name = string("module_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5249216)))]; + tensor module_layers_1_norm1_bias_to_fp16 = const()[name = string("module_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250560)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_layers_1_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5251904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6071168))))[name = string("module_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_layers_1_linear1_bias_to_fp16 = const()[name = string("module_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6073792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_layers_1_linear1_bias_to_fp16, weight = module_layers_1_linear1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = linear_6_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6076416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6895680))))[name = string("module_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_layers_1_linear2_bias_to_fp16 = const()[name = string("module_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6897024)))]; + tensor linear_7_cast_fp16 = linear(bias = module_layers_1_linear2_bias_to_fp16, weight = module_layers_1_linear2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = input_15_cast_fp16, y = linear_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm2_weight_to_fp16 = const()[name = string("module_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6898368)))]; + tensor module_layers_1_norm2_bias_to_fp16 = const()[name = string("module_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6899712)))]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = module_layers_1_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm2_weight_to_fp16, x = input_21_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor module_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8129920))))[name = string("module_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8133824)))]; + tensor linear_8_cast_fp16 = linear(bias = module_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = x_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_398_split_sizes_0 = const()[name = string("op_398_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_398_axis_0 = const()[name = string("op_398_axis_0"), val = int32(-1)]; + tensor var_398_cast_fp16_0, tensor var_398_cast_fp16_1, tensor var_398_cast_fp16_2 = split(axis = var_398_axis_0, split_sizes = var_398_split_sizes_0, x = linear_8_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_401 = const()[name = string("op_401"), val = tensor([1, 256, 4, 160])]; + tensor var_402_cast_fp16 = reshape(shape = var_401, x = var_398_cast_fp16_0)[name = string("op_402_cast_fp16")]; + tensor var_404 = const()[name = string("op_404"), val = tensor([1, 256, 4, 160])]; + tensor var_405_cast_fp16 = reshape(shape = var_404, x = var_398_cast_fp16_1)[name = string("op_405_cast_fp16")]; + tensor var_407 = const()[name = string("op_407"), val = tensor([1, 256, 4, 160])]; + tensor var_408_cast_fp16 = reshape(shape = var_407, x = var_398_cast_fp16_2)[name = string("op_408_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_411_transpose_x_0 = const()[name = string("op_411_transpose_x_0"), val = bool(false)]; + bool var_411_transpose_y_0 = const()[name = string("op_411_transpose_y_0"), val = bool(false)]; + tensor transpose_36_perm_0 = const()[name = string("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_37_perm_0 = const()[name = string("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_405_cast_fp16)[name = string("transpose_70")]; + tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_402_cast_fp16)[name = string("transpose_71")]; + tensor var_411_cast_fp16 = matmul(transpose_x = var_411_transpose_x_0, transpose_y = var_411_transpose_y_0, x = transpose_36, y = transpose_37)[name = string("op_411_cast_fp16")]; + fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_5_cast_fp16 = mul(x = var_411_cast_fp16, y = var_412_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor weights_13_cast_fp16 = softmax(axis = var_24, x = scores_5_cast_fp16)[name = string("weights_13_cast_fp16")]; + tensor weights_15_cast_fp16 = mul(x = weights_13_cast_fp16, y = var_313_cast_fp16)[name = string("weights_15_cast_fp16")]; + tensor var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor([-1])]; + bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)]; + tensor var_423_cast_fp16 = reduce_sum(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = weights_15_cast_fp16)[name = string("op_423_cast_fp16")]; + tensor var_424_cast_fp16 = maximum(x = var_423_cast_fp16, y = var_11_to_fp16)[name = string("op_424_cast_fp16")]; + tensor weights_17_cast_fp16 = real_div(x = weights_15_cast_fp16, y = var_424_cast_fp16)[name = string("weights_17_cast_fp16")]; + bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)]; + bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_408_cast_fp16)[name = string("transpose_69")]; + tensor context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = weights_17_cast_fp16, y = v_11_cast_fp16)[name = string("context_9_cast_fp16")]; + tensor var_427_perm_0 = const()[name = string("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 256, 640])]; + tensor var_427_cast_fp16 = transpose(perm = var_427_perm_0, x = context_9_cast_fp16)[name = string("transpose_68")]; + tensor context_11_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("context_11_cast_fp16")]; + tensor module_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8137728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8547392))))[name = string("module_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548736)))]; + tensor linear_9_cast_fp16 = linear(bias = module_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = context_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = x_7_cast_fp16, y = linear_9_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm1_weight_to_fp16 = const()[name = string("module_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550080)))]; + tensor module_layers_2_norm1_bias_to_fp16 = const()[name = string("module_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8551424)))]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = module_layers_2_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8552768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9372032))))[name = string("module_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_layers_2_linear1_bias_to_fp16 = const()[name = string("module_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9374656)))]; + tensor linear_10_cast_fp16 = linear(bias = module_layers_2_linear1_bias_to_fp16, weight = module_layers_2_linear1_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_29_cast_fp16 = relu(x = linear_10_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor module_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9377280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196544))))[name = string("module_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_layers_2_linear2_bias_to_fp16 = const()[name = string("module_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197888)))]; + tensor linear_11_cast_fp16 = linear(bias = module_layers_2_linear2_bias_to_fp16, weight = module_layers_2_linear2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_25_cast_fp16, y = linear_11_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm2_weight_to_fp16 = const()[name = string("module_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199232)))]; + tensor module_layers_2_norm2_bias_to_fp16 = const()[name = string("module_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10200576)))]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = module_layers_2_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor module_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10201920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11430784))))[name = string("module_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11434688)))]; + tensor linear_12_cast_fp16 = linear(bias = module_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = x_9_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(-1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1, tensor var_451_cast_fp16_2 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = linear_12_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_454 = const()[name = string("op_454"), val = tensor([1, 256, 4, 160])]; + tensor var_455_cast_fp16 = reshape(shape = var_454, x = var_451_cast_fp16_0)[name = string("op_455_cast_fp16")]; + tensor var_457 = const()[name = string("op_457"), val = tensor([1, 256, 4, 160])]; + tensor var_458_cast_fp16 = reshape(shape = var_457, x = var_451_cast_fp16_1)[name = string("op_458_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([1, 256, 4, 160])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = var_451_cast_fp16_2)[name = string("op_461_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_464_transpose_x_0 = const()[name = string("op_464_transpose_x_0"), val = bool(false)]; + bool var_464_transpose_y_0 = const()[name = string("op_464_transpose_y_0"), val = bool(false)]; + tensor transpose_38_perm_0 = const()[name = string("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_39_perm_0 = const()[name = string("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_458_cast_fp16)[name = string("transpose_66")]; + tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_455_cast_fp16)[name = string("transpose_67")]; + tensor var_464_cast_fp16 = matmul(transpose_x = var_464_transpose_x_0, transpose_y = var_464_transpose_y_0, x = transpose_38, y = transpose_39)[name = string("op_464_cast_fp16")]; + fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_7_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("scores_7_cast_fp16")]; + tensor weights_19_cast_fp16 = softmax(axis = var_24, x = scores_7_cast_fp16)[name = string("weights_19_cast_fp16")]; + tensor weights_21_cast_fp16 = mul(x = weights_19_cast_fp16, y = var_313_cast_fp16)[name = string("weights_21_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + bool var_476_keep_dims_0 = const()[name = string("op_476_keep_dims_0"), val = bool(true)]; + tensor var_476_cast_fp16 = reduce_sum(axes = var_476_axes_0, keep_dims = var_476_keep_dims_0, x = weights_21_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor var_477_cast_fp16 = maximum(x = var_476_cast_fp16, y = var_11_to_fp16)[name = string("op_477_cast_fp16")]; + tensor weights_23_cast_fp16 = real_div(x = weights_21_cast_fp16, y = var_477_cast_fp16)[name = string("weights_23_cast_fp16")]; + bool context_13_transpose_x_0 = const()[name = string("context_13_transpose_x_0"), val = bool(false)]; + bool context_13_transpose_y_0 = const()[name = string("context_13_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_461_cast_fp16)[name = string("transpose_65")]; + tensor context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = weights_23_cast_fp16, y = v_15_cast_fp16)[name = string("context_13_cast_fp16")]; + tensor var_480_perm_0 = const()[name = string("op_480_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_481 = const()[name = string("op_481"), val = tensor([1, 256, 640])]; + tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = context_13_cast_fp16)[name = string("transpose_64")]; + tensor context_15_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("context_15_cast_fp16")]; + tensor module_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11438592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11848256))))[name = string("module_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11849600)))]; + tensor linear_13_cast_fp16 = linear(bias = module_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = context_15_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = x_9_cast_fp16, y = linear_13_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_axes_0 = const()[name = string("input_35_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm1_weight_to_fp16 = const()[name = string("module_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11850944)))]; + tensor module_layers_3_norm1_bias_to_fp16 = const()[name = string("module_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11852288)))]; + tensor input_35_cast_fp16 = layer_norm(axes = input_35_axes_0, beta = module_layers_3_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11853632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12672896))))[name = string("module_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_layers_3_linear1_bias_to_fp16 = const()[name = string("module_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12675520)))]; + tensor linear_14_cast_fp16 = linear(bias = module_layers_3_linear1_bias_to_fp16, weight = module_layers_3_linear1_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor input_39_cast_fp16 = relu(x = linear_14_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12678144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13497408))))[name = string("module_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_layers_3_linear2_bias_to_fp16 = const()[name = string("module_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13498752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_layers_3_linear2_bias_to_fp16, weight = module_layers_3_linear2_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm2_weight_to_fp16 = const()[name = string("module_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13500096)))]; + tensor module_layers_3_norm2_bias_to_fp16 = const()[name = string("module_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13501440)))]; + tensor x_11_cast_fp16 = layer_norm(axes = x_11_axes_0, beta = module_layers_3_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm2_weight_to_fp16, x = input_41_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13502784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14731648))))[name = string("module_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14735552)))]; + tensor linear_16_cast_fp16 = linear(bias = module_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_504_split_sizes_0 = const()[name = string("op_504_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_504_axis_0 = const()[name = string("op_504_axis_0"), val = int32(-1)]; + tensor var_504_cast_fp16_0, tensor var_504_cast_fp16_1, tensor var_504_cast_fp16_2 = split(axis = var_504_axis_0, split_sizes = var_504_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_504_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 256, 4, 160])]; + tensor var_508_cast_fp16 = reshape(shape = var_507, x = var_504_cast_fp16_0)[name = string("op_508_cast_fp16")]; + tensor var_510 = const()[name = string("op_510"), val = tensor([1, 256, 4, 160])]; + tensor var_511_cast_fp16 = reshape(shape = var_510, x = var_504_cast_fp16_1)[name = string("op_511_cast_fp16")]; + tensor var_513 = const()[name = string("op_513"), val = tensor([1, 256, 4, 160])]; + tensor var_514_cast_fp16 = reshape(shape = var_513, x = var_504_cast_fp16_2)[name = string("op_514_cast_fp16")]; + tensor v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_517_transpose_x_0 = const()[name = string("op_517_transpose_x_0"), val = bool(false)]; + bool var_517_transpose_y_0 = const()[name = string("op_517_transpose_y_0"), val = bool(false)]; + tensor transpose_40_perm_0 = const()[name = string("transpose_40_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_41_perm_0 = const()[name = string("transpose_41_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_41 = transpose(perm = transpose_41_perm_0, x = var_511_cast_fp16)[name = string("transpose_62")]; + tensor transpose_40 = transpose(perm = transpose_40_perm_0, x = var_508_cast_fp16)[name = string("transpose_63")]; + tensor var_517_cast_fp16 = matmul(transpose_x = var_517_transpose_x_0, transpose_y = var_517_transpose_y_0, x = transpose_40, y = transpose_41)[name = string("op_517_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_9_cast_fp16 = mul(x = var_517_cast_fp16, y = var_518_to_fp16)[name = string("scores_9_cast_fp16")]; + tensor weights_25_cast_fp16 = softmax(axis = var_24, x = scores_9_cast_fp16)[name = string("weights_25_cast_fp16")]; + tensor weights_27_cast_fp16 = mul(x = weights_25_cast_fp16, y = var_313_cast_fp16)[name = string("weights_27_cast_fp16")]; + tensor var_529_axes_0 = const()[name = string("op_529_axes_0"), val = tensor([-1])]; + bool var_529_keep_dims_0 = const()[name = string("op_529_keep_dims_0"), val = bool(true)]; + tensor var_529_cast_fp16 = reduce_sum(axes = var_529_axes_0, keep_dims = var_529_keep_dims_0, x = weights_27_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = maximum(x = var_529_cast_fp16, y = var_11_to_fp16)[name = string("op_530_cast_fp16")]; + tensor weights_29_cast_fp16 = real_div(x = weights_27_cast_fp16, y = var_530_cast_fp16)[name = string("weights_29_cast_fp16")]; + bool context_17_transpose_x_0 = const()[name = string("context_17_transpose_x_0"), val = bool(false)]; + bool context_17_transpose_y_0 = const()[name = string("context_17_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = v_19_perm_0, x = var_514_cast_fp16)[name = string("transpose_61")]; + tensor context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = weights_29_cast_fp16, y = v_19_cast_fp16)[name = string("context_17_cast_fp16")]; + tensor var_533_perm_0 = const()[name = string("op_533_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 256, 640])]; + tensor var_533_cast_fp16 = transpose(perm = var_533_perm_0, x = context_17_cast_fp16)[name = string("transpose_60")]; + tensor context_19_cast_fp16 = reshape(shape = var_534, x = var_533_cast_fp16)[name = string("context_19_cast_fp16")]; + tensor module_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14739456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15149120))))[name = string("module_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15150464)))]; + tensor linear_17_cast_fp16 = linear(bias = module_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = context_19_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = x_11_cast_fp16, y = linear_17_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm1_weight_to_fp16 = const()[name = string("module_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15151808)))]; + tensor module_layers_4_norm1_bias_to_fp16 = const()[name = string("module_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15153152)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = module_layers_4_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15973760))))[name = string("module_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_layers_4_linear1_bias_to_fp16 = const()[name = string("module_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15976384)))]; + tensor linear_18_cast_fp16 = linear(bias = module_layers_4_linear1_bias_to_fp16, weight = module_layers_4_linear1_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = linear_18_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16798272))))[name = string("module_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_layers_4_linear2_bias_to_fp16 = const()[name = string("module_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16799616)))]; + tensor linear_19_cast_fp16 = linear(bias = module_layers_4_linear2_bias_to_fp16, weight = module_layers_4_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_45_cast_fp16, y = linear_19_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm2_weight_to_fp16 = const()[name = string("module_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16800960)))]; + tensor module_layers_4_norm2_bias_to_fp16 = const()[name = string("module_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16802304)))]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = module_layers_4_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm2_weight_to_fp16, x = input_51_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor module_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16803648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18032512))))[name = string("module_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor linear_20_cast_fp16 = linear(bias = module_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = x_13_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_557_split_sizes_0 = const()[name = string("op_557_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_557_axis_0 = const()[name = string("op_557_axis_0"), val = int32(-1)]; + tensor var_557_cast_fp16_0, tensor var_557_cast_fp16_1, tensor var_557_cast_fp16_2 = split(axis = var_557_axis_0, split_sizes = var_557_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_560 = const()[name = string("op_560"), val = tensor([1, 256, 4, 160])]; + tensor var_561_cast_fp16 = reshape(shape = var_560, x = var_557_cast_fp16_0)[name = string("op_561_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([1, 256, 4, 160])]; + tensor var_564_cast_fp16 = reshape(shape = var_563, x = var_557_cast_fp16_1)[name = string("op_564_cast_fp16")]; + tensor var_566 = const()[name = string("op_566"), val = tensor([1, 256, 4, 160])]; + tensor var_567_cast_fp16 = reshape(shape = var_566, x = var_557_cast_fp16_2)[name = string("op_567_cast_fp16")]; + tensor v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_570_transpose_x_0 = const()[name = string("op_570_transpose_x_0"), val = bool(false)]; + bool var_570_transpose_y_0 = const()[name = string("op_570_transpose_y_0"), val = bool(false)]; + tensor transpose_42_perm_0 = const()[name = string("transpose_42_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_43_perm_0 = const()[name = string("transpose_43_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_43 = transpose(perm = transpose_43_perm_0, x = var_564_cast_fp16)[name = string("transpose_58")]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = var_561_cast_fp16)[name = string("transpose_59")]; + tensor var_570_cast_fp16 = matmul(transpose_x = var_570_transpose_x_0, transpose_y = var_570_transpose_y_0, x = transpose_42, y = transpose_43)[name = string("op_570_cast_fp16")]; + fp16 var_571_to_fp16 = const()[name = string("op_571_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_11_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = string("scores_11_cast_fp16")]; + tensor weights_31_cast_fp16 = softmax(axis = var_24, x = scores_11_cast_fp16)[name = string("weights_31_cast_fp16")]; + tensor weights_33_cast_fp16 = mul(x = weights_31_cast_fp16, y = var_313_cast_fp16)[name = string("weights_33_cast_fp16")]; + tensor var_582_axes_0 = const()[name = string("op_582_axes_0"), val = tensor([-1])]; + bool var_582_keep_dims_0 = const()[name = string("op_582_keep_dims_0"), val = bool(true)]; + tensor var_582_cast_fp16 = reduce_sum(axes = var_582_axes_0, keep_dims = var_582_keep_dims_0, x = weights_33_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = maximum(x = var_582_cast_fp16, y = var_11_to_fp16)[name = string("op_583_cast_fp16")]; + tensor weights_35_cast_fp16 = real_div(x = weights_33_cast_fp16, y = var_583_cast_fp16)[name = string("weights_35_cast_fp16")]; + bool context_21_transpose_x_0 = const()[name = string("context_21_transpose_x_0"), val = bool(false)]; + bool context_21_transpose_y_0 = const()[name = string("context_21_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = v_23_perm_0, x = var_567_cast_fp16)[name = string("transpose_57")]; + tensor context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = weights_35_cast_fp16, y = v_23_cast_fp16)[name = string("context_21_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 256, 640])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = context_21_cast_fp16)[name = string("transpose_56")]; + tensor context_23_cast_fp16 = reshape(shape = var_587, x = var_586_cast_fp16)[name = string("context_23_cast_fp16")]; + tensor module_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18040320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18449984))))[name = string("module_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18451328)))]; + tensor linear_21_cast_fp16 = linear(bias = module_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = context_23_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = x_13_cast_fp16, y = linear_21_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm1_weight_to_fp16 = const()[name = string("module_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18452672)))]; + tensor module_layers_5_norm1_bias_to_fp16 = const()[name = string("module_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18454016)))]; + tensor input_55_cast_fp16 = layer_norm(axes = input_55_axes_0, beta = module_layers_5_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm1_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18455360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19274624))))[name = string("module_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_layers_5_linear1_bias_to_fp16 = const()[name = string("module_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19277248)))]; + tensor linear_22_cast_fp16 = linear(bias = module_layers_5_linear1_bias_to_fp16, weight = module_layers_5_linear1_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_59_cast_fp16 = relu(x = linear_22_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19279872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20099136))))[name = string("module_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_layers_5_linear2_bias_to_fp16 = const()[name = string("module_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20100480)))]; + tensor linear_23_cast_fp16 = linear(bias = module_layers_5_linear2_bias_to_fp16, weight = module_layers_5_linear2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_55_cast_fp16, y = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm2_weight_to_fp16 = const()[name = string("module_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20101824)))]; + tensor module_layers_5_norm2_bias_to_fp16 = const()[name = string("module_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20103168)))]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, beta = module_layers_5_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor module_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21333376))))[name = string("module_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21337280)))]; + tensor linear_24_cast_fp16 = linear(bias = module_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = x_15_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_610_split_sizes_0 = const()[name = string("op_610_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_610_axis_0 = const()[name = string("op_610_axis_0"), val = int32(-1)]; + tensor var_610_cast_fp16_0, tensor var_610_cast_fp16_1, tensor var_610_cast_fp16_2 = split(axis = var_610_axis_0, split_sizes = var_610_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor var_613 = const()[name = string("op_613"), val = tensor([1, 256, 4, 160])]; + tensor var_614_cast_fp16 = reshape(shape = var_613, x = var_610_cast_fp16_0)[name = string("op_614_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 256, 4, 160])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_610_cast_fp16_1)[name = string("op_617_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 256, 4, 160])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = var_610_cast_fp16_2)[name = string("op_620_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_623_transpose_x_0 = const()[name = string("op_623_transpose_x_0"), val = bool(false)]; + bool var_623_transpose_y_0 = const()[name = string("op_623_transpose_y_0"), val = bool(false)]; + tensor transpose_44_perm_0 = const()[name = string("transpose_44_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_45_perm_0 = const()[name = string("transpose_45_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_45 = transpose(perm = transpose_45_perm_0, x = var_617_cast_fp16)[name = string("transpose_54")]; + tensor transpose_44 = transpose(perm = transpose_44_perm_0, x = var_614_cast_fp16)[name = string("transpose_55")]; + tensor var_623_cast_fp16 = matmul(transpose_x = var_623_transpose_x_0, transpose_y = var_623_transpose_y_0, x = transpose_44, y = transpose_45)[name = string("op_623_cast_fp16")]; + fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_13_cast_fp16 = mul(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("scores_13_cast_fp16")]; + tensor weights_37_cast_fp16 = softmax(axis = var_24, x = scores_13_cast_fp16)[name = string("weights_37_cast_fp16")]; + tensor weights_39_cast_fp16 = mul(x = weights_37_cast_fp16, y = var_313_cast_fp16)[name = string("weights_39_cast_fp16")]; + tensor var_635_axes_0 = const()[name = string("op_635_axes_0"), val = tensor([-1])]; + bool var_635_keep_dims_0 = const()[name = string("op_635_keep_dims_0"), val = bool(true)]; + tensor var_635_cast_fp16 = reduce_sum(axes = var_635_axes_0, keep_dims = var_635_keep_dims_0, x = weights_39_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_cast_fp16 = maximum(x = var_635_cast_fp16, y = var_11_to_fp16)[name = string("op_636_cast_fp16")]; + tensor weights_41_cast_fp16 = real_div(x = weights_39_cast_fp16, y = var_636_cast_fp16)[name = string("weights_41_cast_fp16")]; + bool context_25_transpose_x_0 = const()[name = string("context_25_transpose_x_0"), val = bool(false)]; + bool context_25_transpose_y_0 = const()[name = string("context_25_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_620_cast_fp16)[name = string("transpose_53")]; + tensor context_25_cast_fp16 = matmul(transpose_x = context_25_transpose_x_0, transpose_y = context_25_transpose_y_0, x = weights_41_cast_fp16, y = v_27_cast_fp16)[name = string("context_25_cast_fp16")]; + tensor var_639_perm_0 = const()[name = string("op_639_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_640 = const()[name = string("op_640"), val = tensor([1, 256, 640])]; + tensor var_639_cast_fp16 = transpose(perm = var_639_perm_0, x = context_25_cast_fp16)[name = string("transpose_52")]; + tensor context_27_cast_fp16 = reshape(shape = var_640, x = var_639_cast_fp16)[name = string("context_27_cast_fp16")]; + tensor module_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21341184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750848))))[name = string("module_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21752192)))]; + tensor linear_25_cast_fp16 = linear(bias = module_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = context_27_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = x_15_cast_fp16, y = linear_25_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm1_weight_to_fp16 = const()[name = string("module_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21753536)))]; + tensor module_layers_6_norm1_bias_to_fp16 = const()[name = string("module_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21754880)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = module_layers_6_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21756224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22575488))))[name = string("module_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_layers_6_linear1_bias_to_fp16 = const()[name = string("module_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22578112)))]; + tensor linear_26_cast_fp16 = linear(bias = module_layers_6_linear1_bias_to_fp16, weight = module_layers_6_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = linear_26_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22580736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23400000))))[name = string("module_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_layers_6_linear2_bias_to_fp16 = const()[name = string("module_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23401344)))]; + tensor linear_27_cast_fp16 = linear(bias = module_layers_6_linear2_bias_to_fp16, weight = module_layers_6_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = input_65_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm2_weight_to_fp16 = const()[name = string("module_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23402688)))]; + tensor module_layers_6_norm2_bias_to_fp16 = const()[name = string("module_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404032)))]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, beta = module_layers_6_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm2_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_cast_fp16")]; + tensor module_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23405376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24634240))))[name = string("module_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24638144)))]; + tensor linear_28_cast_fp16 = linear(bias = module_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = x_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_663_split_sizes_0 = const()[name = string("op_663_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_663_axis_0 = const()[name = string("op_663_axis_0"), val = int32(-1)]; + tensor var_663_cast_fp16_0, tensor var_663_cast_fp16_1, tensor var_663_cast_fp16_2 = split(axis = var_663_axis_0, split_sizes = var_663_split_sizes_0, x = linear_28_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_666 = const()[name = string("op_666"), val = tensor([1, 256, 4, 160])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = var_663_cast_fp16_0)[name = string("op_667_cast_fp16")]; + tensor var_669 = const()[name = string("op_669"), val = tensor([1, 256, 4, 160])]; + tensor var_670_cast_fp16 = reshape(shape = var_669, x = var_663_cast_fp16_1)[name = string("op_670_cast_fp16")]; + tensor var_672 = const()[name = string("op_672"), val = tensor([1, 256, 4, 160])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = var_663_cast_fp16_2)[name = string("op_673_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_676_transpose_x_0 = const()[name = string("op_676_transpose_x_0"), val = bool(false)]; + bool var_676_transpose_y_0 = const()[name = string("op_676_transpose_y_0"), val = bool(false)]; + tensor transpose_46_perm_0 = const()[name = string("transpose_46_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_47_perm_0 = const()[name = string("transpose_47_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_47 = transpose(perm = transpose_47_perm_0, x = var_670_cast_fp16)[name = string("transpose_50")]; + tensor transpose_46 = transpose(perm = transpose_46_perm_0, x = var_667_cast_fp16)[name = string("transpose_51")]; + tensor var_676_cast_fp16 = matmul(transpose_x = var_676_transpose_x_0, transpose_y = var_676_transpose_y_0, x = transpose_46, y = transpose_47)[name = string("op_676_cast_fp16")]; + fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_cast_fp16 = mul(x = var_676_cast_fp16, y = var_677_to_fp16)[name = string("scores_cast_fp16")]; + tensor weights_43_cast_fp16 = softmax(axis = var_24, x = scores_cast_fp16)[name = string("weights_43_cast_fp16")]; + tensor weights_45_cast_fp16 = mul(x = weights_43_cast_fp16, y = var_313_cast_fp16)[name = string("weights_45_cast_fp16")]; + tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([-1])]; + bool var_688_keep_dims_0 = const()[name = string("op_688_keep_dims_0"), val = bool(true)]; + tensor var_688_cast_fp16 = reduce_sum(axes = var_688_axes_0, keep_dims = var_688_keep_dims_0, x = weights_45_cast_fp16)[name = string("op_688_cast_fp16")]; + tensor var_689_cast_fp16 = maximum(x = var_688_cast_fp16, y = var_11_to_fp16)[name = string("op_689_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_45_cast_fp16, y = var_689_cast_fp16)[name = string("weights_cast_fp16")]; + bool context_29_transpose_x_0 = const()[name = string("context_29_transpose_x_0"), val = bool(false)]; + bool context_29_transpose_y_0 = const()[name = string("context_29_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_673_cast_fp16)[name = string("transpose_49")]; + tensor context_29_cast_fp16 = matmul(transpose_x = context_29_transpose_x_0, transpose_y = context_29_transpose_y_0, x = weights_cast_fp16, y = v_cast_fp16)[name = string("context_29_cast_fp16")]; + tensor var_692_perm_0 = const()[name = string("op_692_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_693 = const()[name = string("op_693"), val = tensor([1, 256, 640])]; + tensor var_692_cast_fp16 = transpose(perm = var_692_perm_0, x = context_29_cast_fp16)[name = string("transpose_48")]; + tensor context_cast_fp16 = reshape(shape = var_693, x = var_692_cast_fp16)[name = string("context_cast_fp16")]; + tensor module_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24642048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25051712))))[name = string("module_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25053056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = context_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = x_cast_fp16, y = linear_29_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm1_weight_to_fp16 = const()[name = string("module_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25054400)))]; + tensor module_layers_7_norm1_bias_to_fp16 = const()[name = string("module_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25055744)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_layers_7_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm1_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25057088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25876352))))[name = string("module_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_layers_7_linear1_bias_to_fp16 = const()[name = string("module_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25878976)))]; + tensor linear_30_cast_fp16 = linear(bias = module_layers_7_linear1_bias_to_fp16, weight = module_layers_7_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_79_cast_fp16 = relu(x = linear_30_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25881600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26700864))))[name = string("module_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_layers_7_linear2_bias_to_fp16 = const()[name = string("module_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26702208)))]; + tensor linear_31_cast_fp16 = linear(bias = module_layers_7_linear2_bias_to_fp16, weight = module_layers_7_linear2_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = input_75_cast_fp16, y = linear_31_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor input_83_axes_0 = const()[name = string("input_83_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm2_weight_to_fp16 = const()[name = string("module_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26703552)))]; + tensor module_layers_7_norm2_bias_to_fp16 = const()[name = string("module_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26704896)))]; + tensor input_83_cast_fp16 = layer_norm(axes = input_83_axes_0, beta = module_layers_7_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm2_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([-1])]; + tensor module_encoder_norm_weight_to_fp16 = const()[name = string("module_encoder_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26706240)))]; + tensor module_encoder_norm_bias_to_fp16 = const()[name = string("module_encoder_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26707584)))]; + tensor input_cast_fp16 = layer_norm(axes = input_axes_0, beta = module_encoder_norm_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_encoder_norm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_cast_fp16")]; + tensor module_fc_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26708928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770432))))[name = string("module_fc_out_weight_to_fp16_quantized")]; + tensor module_fc_out_bias_to_fp16 = const()[name = string("module_fc_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770688)))]; + tensor linear_32_cast_fp16 = linear(bias = module_fc_out_bias_to_fp16, weight = module_fc_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor padded_axes_0 = const()[name = string("padded_axes_0"), val = tensor([-1])]; + tensor padded = expand_dims(axes = padded_axes_0, x = src_pad_mask)[name = string("padded")]; + tensor var_723_begin_0 = const()[name = string("op_723_begin_0"), val = tensor([0, 0, 0])]; + tensor var_723_end_0 = const()[name = string("op_723_end_0"), val = tensor([1, 256, 1])]; + tensor var_723_end_mask_0 = const()[name = string("op_723_end_mask_0"), val = tensor([true, true, false])]; + tensor var_723_cast_fp16 = slice_by_index(begin = var_723_begin_0, end = var_723_end_0, end_mask = var_723_end_mask_0, x = linear_32_cast_fp16)[name = string("op_723_cast_fp16")]; + tensor var_722_to_fp16 = const()[name = string("op_722_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27017344)))]; + tensor blank_logits_cast_fp16 = select(a = var_722_to_fp16, b = var_723_cast_fp16, cond = padded)[name = string("blank_logits_cast_fp16")]; + tensor var_725_begin_0 = const()[name = string("op_725_begin_0"), val = tensor([0, 0, 1])]; + tensor var_725_end_0 = const()[name = string("op_725_end_0"), val = tensor([1, 256, 96])]; + tensor var_725_end_mask_0 = const()[name = string("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725_cast_fp16 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = linear_32_cast_fp16)[name = string("op_725_cast_fp16")]; + fp16 var_726_value_0_to_fp16 = const()[name = string("op_726_value_0_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor var_726_cast_fp16 = fill_like(ref_tensor = var_725_cast_fp16, value = var_726_value_0_to_fp16)[name = string("op_726_cast_fp16")]; + tensor padded_after_broadcast_reps_0 = const()[name = string("padded_after_broadcast_reps_0"), val = tensor([1, 1, 95])]; + tensor padded_after_broadcast = tile(reps = padded_after_broadcast_reps_0, x = padded)[name = string("padded_after_broadcast")]; + tensor nonblank_logits_cast_fp16 = select(a = var_726_cast_fp16, b = var_725_cast_fp16, cond = padded_after_broadcast)[name = string("nonblank_logits_cast_fp16")]; + bool var_730_interleave_0 = const()[name = string("op_730_interleave_0"), val = bool(false)]; + tensor var_730_cast_fp16 = concat(axis = var_24, interleave = var_730_interleave_0, values = (blank_logits_cast_fp16, nonblank_logits_cast_fp16))[name = string("op_730_cast_fp16")]; + string var_730_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_730_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_730_cast_fp16_to_fp32_dtype_0, x = var_730_cast_fp16)[name = string("cast_1")]; + } -> (logits); + func g2p_64(tensor text) { + int32 var_24 = const()[name = string("op_24"), val = int32(-1)]; + int32 var_26 = const()[name = string("op_26"), val = int32(0)]; + tensor src_pad_mask = equal(x = text, y = var_26)[name = string("src_pad_mask")]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor module_embedding_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257408))))[name = string("module_embedding_weight_to_fp16_quantized")]; + string text_to_int16_dtype_0 = const()[name = string("text_to_int16_dtype_0"), val = string("int16")]; + string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_to_int16 = cast(dtype = text_to_int16_dtype_0, x = text)[name = string("cast_6")]; + tensor cast_49 = cast(dtype = cast_49_dtype_0, x = text_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_49, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(402)]; + tensor add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_3")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(402)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 x_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_1, validate_indices = x_1_validate_indices_0, x = module_embedding_weight_to_fp16_quantized)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor op_286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258304))), scale = tensor([[[0x1.724p-7]]]))[name = string("op_286_to_fp16_quantized")]; + tensor input_1_cast_fp16 = add(x = x_1_cast_fp16_cast_uint16_cast_uint16, y = op_286_to_fp16_quantized)[name = string("input_1_cast_fp16")]; + tensor module_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528192))))[name = string("module_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1532096)))]; + tensor linear_0_cast_fp16 = linear(bias = module_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_292_split_sizes_0 = const()[name = string("op_292_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_292_axis_0 = const()[name = string("op_292_axis_0"), val = int32(-1)]; + tensor var_292_cast_fp16_0, tensor var_292_cast_fp16_1, tensor var_292_cast_fp16_2 = split(axis = var_292_axis_0, split_sizes = var_292_split_sizes_0, x = linear_0_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor var_295 = const()[name = string("op_295"), val = tensor([1, 64, 4, 160])]; + tensor var_296_cast_fp16 = reshape(shape = var_295, x = var_292_cast_fp16_0)[name = string("op_296_cast_fp16")]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 64, 4, 160])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = var_292_cast_fp16_1)[name = string("op_299_cast_fp16")]; + tensor var_301 = const()[name = string("op_301"), val = tensor([1, 64, 4, 160])]; + tensor var_302_cast_fp16 = reshape(shape = var_301, x = var_292_cast_fp16_2)[name = string("op_302_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_305_transpose_x_0 = const()[name = string("op_305_transpose_x_0"), val = bool(false)]; + bool var_305_transpose_y_0 = const()[name = string("op_305_transpose_y_0"), val = bool(false)]; + tensor transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_299_cast_fp16)[name = string("transpose_78")]; + tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_296_cast_fp16)[name = string("transpose_79")]; + tensor var_305_cast_fp16 = matmul(transpose_x = var_305_transpose_x_0, transpose_y = var_305_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_305_cast_fp16")]; + fp16 var_306_to_fp16 = const()[name = string("op_306_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_1_cast_fp16 = mul(x = var_305_cast_fp16, y = var_306_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor weights_1_cast_fp16 = softmax(axis = var_24, x = scores_1_cast_fp16)[name = string("weights_1_cast_fp16")]; + fp16 var_13_to_fp16 = const()[name = string("op_13_to_fp16"), val = fp16(0x1p+0)]; + string var_309_to_fp16_dtype_0 = const()[name = string("op_309_to_fp16_dtype_0"), val = string("fp16")]; + tensor src_pad_mask_to_fp16 = cast(dtype = var_309_to_fp16_dtype_0, x = src_pad_mask)[name = string("cast_2")]; + tensor var_310_cast_fp16 = sub(x = var_13_to_fp16, y = src_pad_mask_to_fp16)[name = string("op_310_cast_fp16")]; + tensor var_312_axes_0 = const()[name = string("op_312_axes_0"), val = tensor([1])]; + tensor var_312_cast_fp16 = expand_dims(axes = var_312_axes_0, x = var_310_cast_fp16)[name = string("op_312_cast_fp16")]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([2])]; + tensor var_313_cast_fp16 = expand_dims(axes = var_313_axes_0, x = var_312_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor weights_3_cast_fp16 = mul(x = weights_1_cast_fp16, y = var_313_cast_fp16)[name = string("weights_3_cast_fp16")]; + tensor var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor([-1])]; + bool var_317_keep_dims_0 = const()[name = string("op_317_keep_dims_0"), val = bool(true)]; + tensor var_317_cast_fp16 = reduce_sum(axes = var_317_axes_0, keep_dims = var_317_keep_dims_0, x = weights_3_cast_fp16)[name = string("op_317_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_318_cast_fp16 = maximum(x = var_317_cast_fp16, y = var_11_to_fp16)[name = string("op_318_cast_fp16")]; + tensor weights_5_cast_fp16 = real_div(x = weights_3_cast_fp16, y = var_318_cast_fp16)[name = string("weights_5_cast_fp16")]; + bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)]; + bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_302_cast_fp16)[name = string("transpose_77")]; + tensor context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = weights_5_cast_fp16, y = v_3_cast_fp16)[name = string("context_1_cast_fp16")]; + tensor var_321_perm_0 = const()[name = string("op_321_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 64, 640])]; + tensor var_321_cast_fp16 = transpose(perm = var_321_perm_0, x = context_1_cast_fp16)[name = string("transpose_76")]; + tensor context_3_cast_fp16 = reshape(shape = var_322, x = var_321_cast_fp16)[name = string("context_3_cast_fp16")]; + tensor module_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1945664))))[name = string("module_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1947008)))]; + tensor linear_1_cast_fp16 = linear(bias = module_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = context_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = input_1_cast_fp16, y = linear_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm1_weight_to_fp16 = const()[name = string("module_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1948352)))]; + tensor module_layers_0_norm1_bias_to_fp16 = const()[name = string("module_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1949696)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_5_cast_fp16 = layer_norm(axes = input_5_axes_0, beta = module_layers_0_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor module_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1951040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2770304))))[name = string("module_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_layers_0_linear1_bias_to_fp16 = const()[name = string("module_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2772928)))]; + tensor linear_2_cast_fp16 = linear(bias = module_layers_0_linear1_bias_to_fp16, weight = module_layers_0_linear1_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor input_9_cast_fp16 = relu(x = linear_2_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2775552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3594816))))[name = string("module_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_layers_0_linear2_bias_to_fp16 = const()[name = string("module_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3596160)))]; + tensor linear_3_cast_fp16 = linear(bias = module_layers_0_linear2_bias_to_fp16, weight = module_layers_0_linear2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_5_cast_fp16, y = linear_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm2_weight_to_fp16 = const()[name = string("module_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3597504)))]; + tensor module_layers_0_norm2_bias_to_fp16 = const()[name = string("module_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3598848)))]; + tensor x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = module_layers_0_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3600192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4829056))))[name = string("module_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4832960)))]; + tensor linear_4_cast_fp16 = linear(bias = module_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_345_split_sizes_0 = const()[name = string("op_345_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_345_axis_0 = const()[name = string("op_345_axis_0"), val = int32(-1)]; + tensor var_345_cast_fp16_0, tensor var_345_cast_fp16_1, tensor var_345_cast_fp16_2 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = linear_4_cast_fp16)[name = string("op_345_cast_fp16")]; + tensor var_348 = const()[name = string("op_348"), val = tensor([1, 64, 4, 160])]; + tensor var_349_cast_fp16 = reshape(shape = var_348, x = var_345_cast_fp16_0)[name = string("op_349_cast_fp16")]; + tensor var_351 = const()[name = string("op_351"), val = tensor([1, 64, 4, 160])]; + tensor var_352_cast_fp16 = reshape(shape = var_351, x = var_345_cast_fp16_1)[name = string("op_352_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 64, 4, 160])]; + tensor var_355_cast_fp16 = reshape(shape = var_354, x = var_345_cast_fp16_2)[name = string("op_355_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_358_transpose_x_0 = const()[name = string("op_358_transpose_x_0"), val = bool(false)]; + bool var_358_transpose_y_0 = const()[name = string("op_358_transpose_y_0"), val = bool(false)]; + tensor transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_352_cast_fp16)[name = string("transpose_74")]; + tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_349_cast_fp16)[name = string("transpose_75")]; + tensor var_358_cast_fp16 = matmul(transpose_x = var_358_transpose_x_0, transpose_y = var_358_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_3_cast_fp16 = mul(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("scores_3_cast_fp16")]; + tensor weights_7_cast_fp16 = softmax(axis = var_24, x = scores_3_cast_fp16)[name = string("weights_7_cast_fp16")]; + tensor weights_9_cast_fp16 = mul(x = weights_7_cast_fp16, y = var_313_cast_fp16)[name = string("weights_9_cast_fp16")]; + tensor var_370_axes_0 = const()[name = string("op_370_axes_0"), val = tensor([-1])]; + bool var_370_keep_dims_0 = const()[name = string("op_370_keep_dims_0"), val = bool(true)]; + tensor var_370_cast_fp16 = reduce_sum(axes = var_370_axes_0, keep_dims = var_370_keep_dims_0, x = weights_9_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_cast_fp16 = maximum(x = var_370_cast_fp16, y = var_11_to_fp16)[name = string("op_371_cast_fp16")]; + tensor weights_11_cast_fp16 = real_div(x = weights_9_cast_fp16, y = var_371_cast_fp16)[name = string("weights_11_cast_fp16")]; + bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)]; + bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_355_cast_fp16)[name = string("transpose_73")]; + tensor context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = weights_11_cast_fp16, y = v_7_cast_fp16)[name = string("context_5_cast_fp16")]; + tensor var_374_perm_0 = const()[name = string("op_374_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_375 = const()[name = string("op_375"), val = tensor([1, 64, 640])]; + tensor var_374_cast_fp16 = transpose(perm = var_374_perm_0, x = context_5_cast_fp16)[name = string("transpose_72")]; + tensor context_7_cast_fp16 = reshape(shape = var_375, x = var_374_cast_fp16)[name = string("context_7_cast_fp16")]; + tensor module_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4836864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5246528))))[name = string("module_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5247872)))]; + tensor linear_5_cast_fp16 = linear(bias = module_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = context_7_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_5_cast_fp16, y = linear_5_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm1_weight_to_fp16 = const()[name = string("module_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5249216)))]; + tensor module_layers_1_norm1_bias_to_fp16 = const()[name = string("module_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250560)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_layers_1_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5251904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6071168))))[name = string("module_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_layers_1_linear1_bias_to_fp16 = const()[name = string("module_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6073792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_layers_1_linear1_bias_to_fp16, weight = module_layers_1_linear1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = linear_6_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6076416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6895680))))[name = string("module_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_layers_1_linear2_bias_to_fp16 = const()[name = string("module_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6897024)))]; + tensor linear_7_cast_fp16 = linear(bias = module_layers_1_linear2_bias_to_fp16, weight = module_layers_1_linear2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = input_15_cast_fp16, y = linear_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm2_weight_to_fp16 = const()[name = string("module_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6898368)))]; + tensor module_layers_1_norm2_bias_to_fp16 = const()[name = string("module_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6899712)))]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = module_layers_1_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm2_weight_to_fp16, x = input_21_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor module_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8129920))))[name = string("module_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8133824)))]; + tensor linear_8_cast_fp16 = linear(bias = module_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = x_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_398_split_sizes_0 = const()[name = string("op_398_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_398_axis_0 = const()[name = string("op_398_axis_0"), val = int32(-1)]; + tensor var_398_cast_fp16_0, tensor var_398_cast_fp16_1, tensor var_398_cast_fp16_2 = split(axis = var_398_axis_0, split_sizes = var_398_split_sizes_0, x = linear_8_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_401 = const()[name = string("op_401"), val = tensor([1, 64, 4, 160])]; + tensor var_402_cast_fp16 = reshape(shape = var_401, x = var_398_cast_fp16_0)[name = string("op_402_cast_fp16")]; + tensor var_404 = const()[name = string("op_404"), val = tensor([1, 64, 4, 160])]; + tensor var_405_cast_fp16 = reshape(shape = var_404, x = var_398_cast_fp16_1)[name = string("op_405_cast_fp16")]; + tensor var_407 = const()[name = string("op_407"), val = tensor([1, 64, 4, 160])]; + tensor var_408_cast_fp16 = reshape(shape = var_407, x = var_398_cast_fp16_2)[name = string("op_408_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_411_transpose_x_0 = const()[name = string("op_411_transpose_x_0"), val = bool(false)]; + bool var_411_transpose_y_0 = const()[name = string("op_411_transpose_y_0"), val = bool(false)]; + tensor transpose_36_perm_0 = const()[name = string("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_37_perm_0 = const()[name = string("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_405_cast_fp16)[name = string("transpose_70")]; + tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_402_cast_fp16)[name = string("transpose_71")]; + tensor var_411_cast_fp16 = matmul(transpose_x = var_411_transpose_x_0, transpose_y = var_411_transpose_y_0, x = transpose_36, y = transpose_37)[name = string("op_411_cast_fp16")]; + fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_5_cast_fp16 = mul(x = var_411_cast_fp16, y = var_412_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor weights_13_cast_fp16 = softmax(axis = var_24, x = scores_5_cast_fp16)[name = string("weights_13_cast_fp16")]; + tensor weights_15_cast_fp16 = mul(x = weights_13_cast_fp16, y = var_313_cast_fp16)[name = string("weights_15_cast_fp16")]; + tensor var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor([-1])]; + bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)]; + tensor var_423_cast_fp16 = reduce_sum(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = weights_15_cast_fp16)[name = string("op_423_cast_fp16")]; + tensor var_424_cast_fp16 = maximum(x = var_423_cast_fp16, y = var_11_to_fp16)[name = string("op_424_cast_fp16")]; + tensor weights_17_cast_fp16 = real_div(x = weights_15_cast_fp16, y = var_424_cast_fp16)[name = string("weights_17_cast_fp16")]; + bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)]; + bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_408_cast_fp16)[name = string("transpose_69")]; + tensor context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = weights_17_cast_fp16, y = v_11_cast_fp16)[name = string("context_9_cast_fp16")]; + tensor var_427_perm_0 = const()[name = string("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 64, 640])]; + tensor var_427_cast_fp16 = transpose(perm = var_427_perm_0, x = context_9_cast_fp16)[name = string("transpose_68")]; + tensor context_11_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("context_11_cast_fp16")]; + tensor module_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8137728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8547392))))[name = string("module_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548736)))]; + tensor linear_9_cast_fp16 = linear(bias = module_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = context_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = x_7_cast_fp16, y = linear_9_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm1_weight_to_fp16 = const()[name = string("module_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550080)))]; + tensor module_layers_2_norm1_bias_to_fp16 = const()[name = string("module_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8551424)))]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = module_layers_2_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8552768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9372032))))[name = string("module_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_layers_2_linear1_bias_to_fp16 = const()[name = string("module_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9374656)))]; + tensor linear_10_cast_fp16 = linear(bias = module_layers_2_linear1_bias_to_fp16, weight = module_layers_2_linear1_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_29_cast_fp16 = relu(x = linear_10_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor module_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9377280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196544))))[name = string("module_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_layers_2_linear2_bias_to_fp16 = const()[name = string("module_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197888)))]; + tensor linear_11_cast_fp16 = linear(bias = module_layers_2_linear2_bias_to_fp16, weight = module_layers_2_linear2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_25_cast_fp16, y = linear_11_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm2_weight_to_fp16 = const()[name = string("module_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199232)))]; + tensor module_layers_2_norm2_bias_to_fp16 = const()[name = string("module_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10200576)))]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = module_layers_2_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor module_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10201920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11430784))))[name = string("module_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11434688)))]; + tensor linear_12_cast_fp16 = linear(bias = module_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = x_9_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(-1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1, tensor var_451_cast_fp16_2 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = linear_12_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_454 = const()[name = string("op_454"), val = tensor([1, 64, 4, 160])]; + tensor var_455_cast_fp16 = reshape(shape = var_454, x = var_451_cast_fp16_0)[name = string("op_455_cast_fp16")]; + tensor var_457 = const()[name = string("op_457"), val = tensor([1, 64, 4, 160])]; + tensor var_458_cast_fp16 = reshape(shape = var_457, x = var_451_cast_fp16_1)[name = string("op_458_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([1, 64, 4, 160])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = var_451_cast_fp16_2)[name = string("op_461_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_464_transpose_x_0 = const()[name = string("op_464_transpose_x_0"), val = bool(false)]; + bool var_464_transpose_y_0 = const()[name = string("op_464_transpose_y_0"), val = bool(false)]; + tensor transpose_38_perm_0 = const()[name = string("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_39_perm_0 = const()[name = string("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_458_cast_fp16)[name = string("transpose_66")]; + tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_455_cast_fp16)[name = string("transpose_67")]; + tensor var_464_cast_fp16 = matmul(transpose_x = var_464_transpose_x_0, transpose_y = var_464_transpose_y_0, x = transpose_38, y = transpose_39)[name = string("op_464_cast_fp16")]; + fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_7_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("scores_7_cast_fp16")]; + tensor weights_19_cast_fp16 = softmax(axis = var_24, x = scores_7_cast_fp16)[name = string("weights_19_cast_fp16")]; + tensor weights_21_cast_fp16 = mul(x = weights_19_cast_fp16, y = var_313_cast_fp16)[name = string("weights_21_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + bool var_476_keep_dims_0 = const()[name = string("op_476_keep_dims_0"), val = bool(true)]; + tensor var_476_cast_fp16 = reduce_sum(axes = var_476_axes_0, keep_dims = var_476_keep_dims_0, x = weights_21_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor var_477_cast_fp16 = maximum(x = var_476_cast_fp16, y = var_11_to_fp16)[name = string("op_477_cast_fp16")]; + tensor weights_23_cast_fp16 = real_div(x = weights_21_cast_fp16, y = var_477_cast_fp16)[name = string("weights_23_cast_fp16")]; + bool context_13_transpose_x_0 = const()[name = string("context_13_transpose_x_0"), val = bool(false)]; + bool context_13_transpose_y_0 = const()[name = string("context_13_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_461_cast_fp16)[name = string("transpose_65")]; + tensor context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = weights_23_cast_fp16, y = v_15_cast_fp16)[name = string("context_13_cast_fp16")]; + tensor var_480_perm_0 = const()[name = string("op_480_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_481 = const()[name = string("op_481"), val = tensor([1, 64, 640])]; + tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = context_13_cast_fp16)[name = string("transpose_64")]; + tensor context_15_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("context_15_cast_fp16")]; + tensor module_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11438592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11848256))))[name = string("module_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11849600)))]; + tensor linear_13_cast_fp16 = linear(bias = module_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = context_15_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = x_9_cast_fp16, y = linear_13_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_axes_0 = const()[name = string("input_35_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm1_weight_to_fp16 = const()[name = string("module_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11850944)))]; + tensor module_layers_3_norm1_bias_to_fp16 = const()[name = string("module_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11852288)))]; + tensor input_35_cast_fp16 = layer_norm(axes = input_35_axes_0, beta = module_layers_3_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11853632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12672896))))[name = string("module_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_layers_3_linear1_bias_to_fp16 = const()[name = string("module_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12675520)))]; + tensor linear_14_cast_fp16 = linear(bias = module_layers_3_linear1_bias_to_fp16, weight = module_layers_3_linear1_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor input_39_cast_fp16 = relu(x = linear_14_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12678144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13497408))))[name = string("module_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_layers_3_linear2_bias_to_fp16 = const()[name = string("module_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13498752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_layers_3_linear2_bias_to_fp16, weight = module_layers_3_linear2_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm2_weight_to_fp16 = const()[name = string("module_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13500096)))]; + tensor module_layers_3_norm2_bias_to_fp16 = const()[name = string("module_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13501440)))]; + tensor x_11_cast_fp16 = layer_norm(axes = x_11_axes_0, beta = module_layers_3_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm2_weight_to_fp16, x = input_41_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13502784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14731648))))[name = string("module_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14735552)))]; + tensor linear_16_cast_fp16 = linear(bias = module_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_504_split_sizes_0 = const()[name = string("op_504_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_504_axis_0 = const()[name = string("op_504_axis_0"), val = int32(-1)]; + tensor var_504_cast_fp16_0, tensor var_504_cast_fp16_1, tensor var_504_cast_fp16_2 = split(axis = var_504_axis_0, split_sizes = var_504_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_504_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 64, 4, 160])]; + tensor var_508_cast_fp16 = reshape(shape = var_507, x = var_504_cast_fp16_0)[name = string("op_508_cast_fp16")]; + tensor var_510 = const()[name = string("op_510"), val = tensor([1, 64, 4, 160])]; + tensor var_511_cast_fp16 = reshape(shape = var_510, x = var_504_cast_fp16_1)[name = string("op_511_cast_fp16")]; + tensor var_513 = const()[name = string("op_513"), val = tensor([1, 64, 4, 160])]; + tensor var_514_cast_fp16 = reshape(shape = var_513, x = var_504_cast_fp16_2)[name = string("op_514_cast_fp16")]; + tensor v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_517_transpose_x_0 = const()[name = string("op_517_transpose_x_0"), val = bool(false)]; + bool var_517_transpose_y_0 = const()[name = string("op_517_transpose_y_0"), val = bool(false)]; + tensor transpose_40_perm_0 = const()[name = string("transpose_40_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_41_perm_0 = const()[name = string("transpose_41_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_41 = transpose(perm = transpose_41_perm_0, x = var_511_cast_fp16)[name = string("transpose_62")]; + tensor transpose_40 = transpose(perm = transpose_40_perm_0, x = var_508_cast_fp16)[name = string("transpose_63")]; + tensor var_517_cast_fp16 = matmul(transpose_x = var_517_transpose_x_0, transpose_y = var_517_transpose_y_0, x = transpose_40, y = transpose_41)[name = string("op_517_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_9_cast_fp16 = mul(x = var_517_cast_fp16, y = var_518_to_fp16)[name = string("scores_9_cast_fp16")]; + tensor weights_25_cast_fp16 = softmax(axis = var_24, x = scores_9_cast_fp16)[name = string("weights_25_cast_fp16")]; + tensor weights_27_cast_fp16 = mul(x = weights_25_cast_fp16, y = var_313_cast_fp16)[name = string("weights_27_cast_fp16")]; + tensor var_529_axes_0 = const()[name = string("op_529_axes_0"), val = tensor([-1])]; + bool var_529_keep_dims_0 = const()[name = string("op_529_keep_dims_0"), val = bool(true)]; + tensor var_529_cast_fp16 = reduce_sum(axes = var_529_axes_0, keep_dims = var_529_keep_dims_0, x = weights_27_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = maximum(x = var_529_cast_fp16, y = var_11_to_fp16)[name = string("op_530_cast_fp16")]; + tensor weights_29_cast_fp16 = real_div(x = weights_27_cast_fp16, y = var_530_cast_fp16)[name = string("weights_29_cast_fp16")]; + bool context_17_transpose_x_0 = const()[name = string("context_17_transpose_x_0"), val = bool(false)]; + bool context_17_transpose_y_0 = const()[name = string("context_17_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = v_19_perm_0, x = var_514_cast_fp16)[name = string("transpose_61")]; + tensor context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = weights_29_cast_fp16, y = v_19_cast_fp16)[name = string("context_17_cast_fp16")]; + tensor var_533_perm_0 = const()[name = string("op_533_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 64, 640])]; + tensor var_533_cast_fp16 = transpose(perm = var_533_perm_0, x = context_17_cast_fp16)[name = string("transpose_60")]; + tensor context_19_cast_fp16 = reshape(shape = var_534, x = var_533_cast_fp16)[name = string("context_19_cast_fp16")]; + tensor module_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14739456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15149120))))[name = string("module_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15150464)))]; + tensor linear_17_cast_fp16 = linear(bias = module_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = context_19_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = x_11_cast_fp16, y = linear_17_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm1_weight_to_fp16 = const()[name = string("module_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15151808)))]; + tensor module_layers_4_norm1_bias_to_fp16 = const()[name = string("module_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15153152)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = module_layers_4_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15973760))))[name = string("module_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_layers_4_linear1_bias_to_fp16 = const()[name = string("module_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15976384)))]; + tensor linear_18_cast_fp16 = linear(bias = module_layers_4_linear1_bias_to_fp16, weight = module_layers_4_linear1_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = linear_18_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16798272))))[name = string("module_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_layers_4_linear2_bias_to_fp16 = const()[name = string("module_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16799616)))]; + tensor linear_19_cast_fp16 = linear(bias = module_layers_4_linear2_bias_to_fp16, weight = module_layers_4_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_45_cast_fp16, y = linear_19_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm2_weight_to_fp16 = const()[name = string("module_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16800960)))]; + tensor module_layers_4_norm2_bias_to_fp16 = const()[name = string("module_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16802304)))]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = module_layers_4_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm2_weight_to_fp16, x = input_51_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor module_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16803648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18032512))))[name = string("module_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor linear_20_cast_fp16 = linear(bias = module_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = x_13_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_557_split_sizes_0 = const()[name = string("op_557_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_557_axis_0 = const()[name = string("op_557_axis_0"), val = int32(-1)]; + tensor var_557_cast_fp16_0, tensor var_557_cast_fp16_1, tensor var_557_cast_fp16_2 = split(axis = var_557_axis_0, split_sizes = var_557_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_560 = const()[name = string("op_560"), val = tensor([1, 64, 4, 160])]; + tensor var_561_cast_fp16 = reshape(shape = var_560, x = var_557_cast_fp16_0)[name = string("op_561_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([1, 64, 4, 160])]; + tensor var_564_cast_fp16 = reshape(shape = var_563, x = var_557_cast_fp16_1)[name = string("op_564_cast_fp16")]; + tensor var_566 = const()[name = string("op_566"), val = tensor([1, 64, 4, 160])]; + tensor var_567_cast_fp16 = reshape(shape = var_566, x = var_557_cast_fp16_2)[name = string("op_567_cast_fp16")]; + tensor v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_570_transpose_x_0 = const()[name = string("op_570_transpose_x_0"), val = bool(false)]; + bool var_570_transpose_y_0 = const()[name = string("op_570_transpose_y_0"), val = bool(false)]; + tensor transpose_42_perm_0 = const()[name = string("transpose_42_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_43_perm_0 = const()[name = string("transpose_43_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_43 = transpose(perm = transpose_43_perm_0, x = var_564_cast_fp16)[name = string("transpose_58")]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = var_561_cast_fp16)[name = string("transpose_59")]; + tensor var_570_cast_fp16 = matmul(transpose_x = var_570_transpose_x_0, transpose_y = var_570_transpose_y_0, x = transpose_42, y = transpose_43)[name = string("op_570_cast_fp16")]; + fp16 var_571_to_fp16 = const()[name = string("op_571_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_11_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = string("scores_11_cast_fp16")]; + tensor weights_31_cast_fp16 = softmax(axis = var_24, x = scores_11_cast_fp16)[name = string("weights_31_cast_fp16")]; + tensor weights_33_cast_fp16 = mul(x = weights_31_cast_fp16, y = var_313_cast_fp16)[name = string("weights_33_cast_fp16")]; + tensor var_582_axes_0 = const()[name = string("op_582_axes_0"), val = tensor([-1])]; + bool var_582_keep_dims_0 = const()[name = string("op_582_keep_dims_0"), val = bool(true)]; + tensor var_582_cast_fp16 = reduce_sum(axes = var_582_axes_0, keep_dims = var_582_keep_dims_0, x = weights_33_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = maximum(x = var_582_cast_fp16, y = var_11_to_fp16)[name = string("op_583_cast_fp16")]; + tensor weights_35_cast_fp16 = real_div(x = weights_33_cast_fp16, y = var_583_cast_fp16)[name = string("weights_35_cast_fp16")]; + bool context_21_transpose_x_0 = const()[name = string("context_21_transpose_x_0"), val = bool(false)]; + bool context_21_transpose_y_0 = const()[name = string("context_21_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = v_23_perm_0, x = var_567_cast_fp16)[name = string("transpose_57")]; + tensor context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = weights_35_cast_fp16, y = v_23_cast_fp16)[name = string("context_21_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 64, 640])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = context_21_cast_fp16)[name = string("transpose_56")]; + tensor context_23_cast_fp16 = reshape(shape = var_587, x = var_586_cast_fp16)[name = string("context_23_cast_fp16")]; + tensor module_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18040320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18449984))))[name = string("module_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18451328)))]; + tensor linear_21_cast_fp16 = linear(bias = module_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = context_23_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = x_13_cast_fp16, y = linear_21_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm1_weight_to_fp16 = const()[name = string("module_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18452672)))]; + tensor module_layers_5_norm1_bias_to_fp16 = const()[name = string("module_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18454016)))]; + tensor input_55_cast_fp16 = layer_norm(axes = input_55_axes_0, beta = module_layers_5_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm1_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18455360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19274624))))[name = string("module_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_layers_5_linear1_bias_to_fp16 = const()[name = string("module_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19277248)))]; + tensor linear_22_cast_fp16 = linear(bias = module_layers_5_linear1_bias_to_fp16, weight = module_layers_5_linear1_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_59_cast_fp16 = relu(x = linear_22_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19279872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20099136))))[name = string("module_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_layers_5_linear2_bias_to_fp16 = const()[name = string("module_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20100480)))]; + tensor linear_23_cast_fp16 = linear(bias = module_layers_5_linear2_bias_to_fp16, weight = module_layers_5_linear2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_55_cast_fp16, y = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm2_weight_to_fp16 = const()[name = string("module_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20101824)))]; + tensor module_layers_5_norm2_bias_to_fp16 = const()[name = string("module_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20103168)))]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, beta = module_layers_5_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor module_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21333376))))[name = string("module_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21337280)))]; + tensor linear_24_cast_fp16 = linear(bias = module_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = x_15_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_610_split_sizes_0 = const()[name = string("op_610_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_610_axis_0 = const()[name = string("op_610_axis_0"), val = int32(-1)]; + tensor var_610_cast_fp16_0, tensor var_610_cast_fp16_1, tensor var_610_cast_fp16_2 = split(axis = var_610_axis_0, split_sizes = var_610_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor var_613 = const()[name = string("op_613"), val = tensor([1, 64, 4, 160])]; + tensor var_614_cast_fp16 = reshape(shape = var_613, x = var_610_cast_fp16_0)[name = string("op_614_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 64, 4, 160])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_610_cast_fp16_1)[name = string("op_617_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 64, 4, 160])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = var_610_cast_fp16_2)[name = string("op_620_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_623_transpose_x_0 = const()[name = string("op_623_transpose_x_0"), val = bool(false)]; + bool var_623_transpose_y_0 = const()[name = string("op_623_transpose_y_0"), val = bool(false)]; + tensor transpose_44_perm_0 = const()[name = string("transpose_44_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_45_perm_0 = const()[name = string("transpose_45_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_45 = transpose(perm = transpose_45_perm_0, x = var_617_cast_fp16)[name = string("transpose_54")]; + tensor transpose_44 = transpose(perm = transpose_44_perm_0, x = var_614_cast_fp16)[name = string("transpose_55")]; + tensor var_623_cast_fp16 = matmul(transpose_x = var_623_transpose_x_0, transpose_y = var_623_transpose_y_0, x = transpose_44, y = transpose_45)[name = string("op_623_cast_fp16")]; + fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_13_cast_fp16 = mul(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("scores_13_cast_fp16")]; + tensor weights_37_cast_fp16 = softmax(axis = var_24, x = scores_13_cast_fp16)[name = string("weights_37_cast_fp16")]; + tensor weights_39_cast_fp16 = mul(x = weights_37_cast_fp16, y = var_313_cast_fp16)[name = string("weights_39_cast_fp16")]; + tensor var_635_axes_0 = const()[name = string("op_635_axes_0"), val = tensor([-1])]; + bool var_635_keep_dims_0 = const()[name = string("op_635_keep_dims_0"), val = bool(true)]; + tensor var_635_cast_fp16 = reduce_sum(axes = var_635_axes_0, keep_dims = var_635_keep_dims_0, x = weights_39_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_cast_fp16 = maximum(x = var_635_cast_fp16, y = var_11_to_fp16)[name = string("op_636_cast_fp16")]; + tensor weights_41_cast_fp16 = real_div(x = weights_39_cast_fp16, y = var_636_cast_fp16)[name = string("weights_41_cast_fp16")]; + bool context_25_transpose_x_0 = const()[name = string("context_25_transpose_x_0"), val = bool(false)]; + bool context_25_transpose_y_0 = const()[name = string("context_25_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_620_cast_fp16)[name = string("transpose_53")]; + tensor context_25_cast_fp16 = matmul(transpose_x = context_25_transpose_x_0, transpose_y = context_25_transpose_y_0, x = weights_41_cast_fp16, y = v_27_cast_fp16)[name = string("context_25_cast_fp16")]; + tensor var_639_perm_0 = const()[name = string("op_639_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_640 = const()[name = string("op_640"), val = tensor([1, 64, 640])]; + tensor var_639_cast_fp16 = transpose(perm = var_639_perm_0, x = context_25_cast_fp16)[name = string("transpose_52")]; + tensor context_27_cast_fp16 = reshape(shape = var_640, x = var_639_cast_fp16)[name = string("context_27_cast_fp16")]; + tensor module_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21341184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750848))))[name = string("module_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21752192)))]; + tensor linear_25_cast_fp16 = linear(bias = module_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = context_27_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = x_15_cast_fp16, y = linear_25_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm1_weight_to_fp16 = const()[name = string("module_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21753536)))]; + tensor module_layers_6_norm1_bias_to_fp16 = const()[name = string("module_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21754880)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = module_layers_6_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21756224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22575488))))[name = string("module_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_layers_6_linear1_bias_to_fp16 = const()[name = string("module_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22578112)))]; + tensor linear_26_cast_fp16 = linear(bias = module_layers_6_linear1_bias_to_fp16, weight = module_layers_6_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = linear_26_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22580736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23400000))))[name = string("module_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_layers_6_linear2_bias_to_fp16 = const()[name = string("module_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23401344)))]; + tensor linear_27_cast_fp16 = linear(bias = module_layers_6_linear2_bias_to_fp16, weight = module_layers_6_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = input_65_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm2_weight_to_fp16 = const()[name = string("module_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23402688)))]; + tensor module_layers_6_norm2_bias_to_fp16 = const()[name = string("module_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404032)))]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, beta = module_layers_6_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm2_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_cast_fp16")]; + tensor module_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23405376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24634240))))[name = string("module_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24638144)))]; + tensor linear_28_cast_fp16 = linear(bias = module_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = x_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_663_split_sizes_0 = const()[name = string("op_663_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_663_axis_0 = const()[name = string("op_663_axis_0"), val = int32(-1)]; + tensor var_663_cast_fp16_0, tensor var_663_cast_fp16_1, tensor var_663_cast_fp16_2 = split(axis = var_663_axis_0, split_sizes = var_663_split_sizes_0, x = linear_28_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_666 = const()[name = string("op_666"), val = tensor([1, 64, 4, 160])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = var_663_cast_fp16_0)[name = string("op_667_cast_fp16")]; + tensor var_669 = const()[name = string("op_669"), val = tensor([1, 64, 4, 160])]; + tensor var_670_cast_fp16 = reshape(shape = var_669, x = var_663_cast_fp16_1)[name = string("op_670_cast_fp16")]; + tensor var_672 = const()[name = string("op_672"), val = tensor([1, 64, 4, 160])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = var_663_cast_fp16_2)[name = string("op_673_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_676_transpose_x_0 = const()[name = string("op_676_transpose_x_0"), val = bool(false)]; + bool var_676_transpose_y_0 = const()[name = string("op_676_transpose_y_0"), val = bool(false)]; + tensor transpose_46_perm_0 = const()[name = string("transpose_46_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_47_perm_0 = const()[name = string("transpose_47_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_47 = transpose(perm = transpose_47_perm_0, x = var_670_cast_fp16)[name = string("transpose_50")]; + tensor transpose_46 = transpose(perm = transpose_46_perm_0, x = var_667_cast_fp16)[name = string("transpose_51")]; + tensor var_676_cast_fp16 = matmul(transpose_x = var_676_transpose_x_0, transpose_y = var_676_transpose_y_0, x = transpose_46, y = transpose_47)[name = string("op_676_cast_fp16")]; + fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_cast_fp16 = mul(x = var_676_cast_fp16, y = var_677_to_fp16)[name = string("scores_cast_fp16")]; + tensor weights_43_cast_fp16 = softmax(axis = var_24, x = scores_cast_fp16)[name = string("weights_43_cast_fp16")]; + tensor weights_45_cast_fp16 = mul(x = weights_43_cast_fp16, y = var_313_cast_fp16)[name = string("weights_45_cast_fp16")]; + tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([-1])]; + bool var_688_keep_dims_0 = const()[name = string("op_688_keep_dims_0"), val = bool(true)]; + tensor var_688_cast_fp16 = reduce_sum(axes = var_688_axes_0, keep_dims = var_688_keep_dims_0, x = weights_45_cast_fp16)[name = string("op_688_cast_fp16")]; + tensor var_689_cast_fp16 = maximum(x = var_688_cast_fp16, y = var_11_to_fp16)[name = string("op_689_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_45_cast_fp16, y = var_689_cast_fp16)[name = string("weights_cast_fp16")]; + bool context_29_transpose_x_0 = const()[name = string("context_29_transpose_x_0"), val = bool(false)]; + bool context_29_transpose_y_0 = const()[name = string("context_29_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_673_cast_fp16)[name = string("transpose_49")]; + tensor context_29_cast_fp16 = matmul(transpose_x = context_29_transpose_x_0, transpose_y = context_29_transpose_y_0, x = weights_cast_fp16, y = v_cast_fp16)[name = string("context_29_cast_fp16")]; + tensor var_692_perm_0 = const()[name = string("op_692_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_693 = const()[name = string("op_693"), val = tensor([1, 64, 640])]; + tensor var_692_cast_fp16 = transpose(perm = var_692_perm_0, x = context_29_cast_fp16)[name = string("transpose_48")]; + tensor context_cast_fp16 = reshape(shape = var_693, x = var_692_cast_fp16)[name = string("context_cast_fp16")]; + tensor module_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24642048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25051712))))[name = string("module_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25053056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = context_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = x_cast_fp16, y = linear_29_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm1_weight_to_fp16 = const()[name = string("module_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25054400)))]; + tensor module_layers_7_norm1_bias_to_fp16 = const()[name = string("module_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25055744)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_layers_7_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm1_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25057088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25876352))))[name = string("module_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_layers_7_linear1_bias_to_fp16 = const()[name = string("module_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25878976)))]; + tensor linear_30_cast_fp16 = linear(bias = module_layers_7_linear1_bias_to_fp16, weight = module_layers_7_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_79_cast_fp16 = relu(x = linear_30_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25881600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26700864))))[name = string("module_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_layers_7_linear2_bias_to_fp16 = const()[name = string("module_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26702208)))]; + tensor linear_31_cast_fp16 = linear(bias = module_layers_7_linear2_bias_to_fp16, weight = module_layers_7_linear2_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = input_75_cast_fp16, y = linear_31_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor input_83_axes_0 = const()[name = string("input_83_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm2_weight_to_fp16 = const()[name = string("module_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26703552)))]; + tensor module_layers_7_norm2_bias_to_fp16 = const()[name = string("module_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26704896)))]; + tensor input_83_cast_fp16 = layer_norm(axes = input_83_axes_0, beta = module_layers_7_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm2_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([-1])]; + tensor module_encoder_norm_weight_to_fp16 = const()[name = string("module_encoder_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26706240)))]; + tensor module_encoder_norm_bias_to_fp16 = const()[name = string("module_encoder_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26707584)))]; + tensor input_cast_fp16 = layer_norm(axes = input_axes_0, beta = module_encoder_norm_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_encoder_norm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_cast_fp16")]; + tensor module_fc_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26708928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770432))))[name = string("module_fc_out_weight_to_fp16_quantized")]; + tensor module_fc_out_bias_to_fp16 = const()[name = string("module_fc_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770688)))]; + tensor linear_32_cast_fp16 = linear(bias = module_fc_out_bias_to_fp16, weight = module_fc_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor padded_axes_0 = const()[name = string("padded_axes_0"), val = tensor([-1])]; + tensor padded = expand_dims(axes = padded_axes_0, x = src_pad_mask)[name = string("padded")]; + tensor var_723_begin_0 = const()[name = string("op_723_begin_0"), val = tensor([0, 0, 0])]; + tensor var_723_end_0 = const()[name = string("op_723_end_0"), val = tensor([1, 64, 1])]; + tensor var_723_end_mask_0 = const()[name = string("op_723_end_mask_0"), val = tensor([true, true, false])]; + tensor var_723_cast_fp16 = slice_by_index(begin = var_723_begin_0, end = var_723_end_0, end_mask = var_723_end_mask_0, x = linear_32_cast_fp16)[name = string("op_723_cast_fp16")]; + tensor var_722_to_fp16 = const()[name = string("op_722_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770944)))]; + tensor blank_logits_cast_fp16 = select(a = var_722_to_fp16, b = var_723_cast_fp16, cond = padded)[name = string("blank_logits_cast_fp16")]; + tensor var_725_begin_0 = const()[name = string("op_725_begin_0"), val = tensor([0, 0, 1])]; + tensor var_725_end_0 = const()[name = string("op_725_end_0"), val = tensor([1, 64, 96])]; + tensor var_725_end_mask_0 = const()[name = string("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725_cast_fp16 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = linear_32_cast_fp16)[name = string("op_725_cast_fp16")]; + fp16 var_726_value_0_to_fp16 = const()[name = string("op_726_value_0_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor var_726_cast_fp16 = fill_like(ref_tensor = var_725_cast_fp16, value = var_726_value_0_to_fp16)[name = string("op_726_cast_fp16")]; + tensor padded_after_broadcast_reps_0 = const()[name = string("padded_after_broadcast_reps_0"), val = tensor([1, 1, 95])]; + tensor padded_after_broadcast = tile(reps = padded_after_broadcast_reps_0, x = padded)[name = string("padded_after_broadcast")]; + tensor nonblank_logits_cast_fp16 = select(a = var_726_cast_fp16, b = var_725_cast_fp16, cond = padded_after_broadcast)[name = string("nonblank_logits_cast_fp16")]; + bool var_730_interleave_0 = const()[name = string("op_730_interleave_0"), val = bool(false)]; + tensor var_730_cast_fp16 = concat(axis = var_24, interleave = var_730_interleave_0, values = (blank_logits_cast_fp16, nonblank_logits_cast_fp16))[name = string("op_730_cast_fp16")]; + string var_730_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_730_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_730_cast_fp16_to_fp32_dtype_0, x = var_730_cast_fp16)[name = string("cast_1")]; + } -> (logits); +} \ No newline at end of file diff --git a/coreml/coreml/g2p.mlmodelc/weights/weight.bin b/coreml/coreml/g2p.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..dd5c633871d0edfb7a0f555b71498a0a2b9ac49b --- /dev/null +++ b/coreml/coreml/g2p.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:045da2991ed2a42ba027aaa6b83813054f410b6eedfdce82f2ab287ec4ca74e2 +size 27346752 diff --git a/coreml/coreml/pitch_context_encoder.mlmodelc/analytics/coremldata.bin b/coreml/coreml/pitch_context_encoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..95ffdd9247224bad86aa3e260f76bd8723a4e2e1 --- /dev/null +++ b/coreml/coreml/pitch_context_encoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ef86aaa3ec78ce0ff02c2927c02dcb1d073d64ddd6b78e4358af91cbd0842d2 +size 243 diff --git a/coreml/coreml/pitch_context_encoder.mlmodelc/coremldata.bin b/coreml/coreml/pitch_context_encoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..f0e1f2ff94ac81626ba6f479b67c909299809d7b --- /dev/null +++ b/coreml/coreml/pitch_context_encoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e77e8333bd4ccb529ba0b96fc342cece9114686fc381837dac32e354a5ee50d +size 476 diff --git a/coreml/coreml/pitch_context_encoder.mlmodelc/model.mil b/coreml/coreml/pitch_context_encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..1a31b7795da79221a08d4a71f1a375380b21873a --- /dev/null +++ b/coreml/coreml/pitch_context_encoder.mlmodelc/model.mil @@ -0,0 +1,267 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor span_context_mask, tensor span_context_phone_ids, tensor span_context_segment_ids) { + int32 var_15 = const()[name = string("op_15"), val = int32(0)]; + tensor span_context_mask_1 = not_equal(x = span_context_mask, y = var_15)[name = string("span_context_mask")]; + int32 var_30 = const()[name = string("op_30"), val = int32(-1)]; + int32 var_35 = const()[name = string("op_35"), val = int32(2)]; + int32 var_36 = const()[name = string("op_36"), val = int32(86)]; + int32 var_38 = const()[name = string("op_38"), val = int32(1)]; + int32 var_40 = const()[name = string("op_40"), val = int32(0)]; + tensor var_55 = maximum(x = span_context_phone_ids, y = var_40)[name = string("op_55")]; + tensor input_1 = minimum(x = var_55, y = var_36)[name = string("input_1")]; + tensor var_59 = maximum(x = span_context_segment_ids, y = var_40)[name = string("op_59")]; + tensor input_3 = minimum(x = var_59, y = var_35)[name = string("input_3")]; + string var_64_dtype_0 = const()[name = string("op_64_dtype_0"), val = string("int32")]; + bool var_65_exclusive_0 = const()[name = string("op_65_exclusive_0"), val = bool(false)]; + bool var_65_reverse_0 = const()[name = string("op_65_reverse_0"), val = bool(false)]; + tensor var_64 = cast(dtype = var_64_dtype_0, x = span_context_mask_1)[name = string("cast_13")]; + tensor var_65 = cumsum(axis = var_38, exclusive = var_65_exclusive_0, reverse = var_65_reverse_0, x = var_64)[name = string("op_65")]; + int32 var_66 = const()[name = string("op_66"), val = int32(1)]; + tensor var_67 = sub(x = var_65, y = var_66)[name = string("op_67")]; + tensor input_5 = maximum(x = var_67, y = var_40)[name = string("input_5")]; + int32 var_70_batch_dims_0 = const()[name = string("op_70_batch_dims_0"), val = int32(0)]; + bool var_70_validate_indices_0 = const()[name = string("op_70_validate_indices_0"), val = bool(false)]; + tensor module_flow_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33536))))[name = string("module_flow_phone_embed_weight_to_fp16_quantized")]; + string input_1_to_int16_dtype_0 = const()[name = string("input_1_to_int16_dtype_0"), val = string("int16")]; + string cast_24_dtype_0 = const()[name = string("cast_24_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor input_1_to_int16 = cast(dtype = input_1_to_int16_dtype_0, x = input_1)[name = string("cast_12")]; + tensor cast_24 = cast(dtype = cast_24_dtype_0, x = input_1_to_int16)[name = string("cast_11")]; + tensor greater_equal_0 = greater_equal(x = cast_24, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(87)]; + tensor add_0 = add(x = cast_24, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_24, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_10")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_9")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(87)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_70_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_70_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_70_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_70_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_70_batch_dims_0, indices = select_0_1, validate_indices = var_70_validate_indices_0, x = module_flow_phone_embed_weight_to_fp16_quantized)[name = string("op_70_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_72_axis_0 = const()[name = string("op_72_axis_0"), val = int32(0)]; + int32 var_72_batch_dims_0 = const()[name = string("op_72_batch_dims_0"), val = int32(0)]; + bool var_72_validate_indices_0 = const()[name = string("op_72_validate_indices_0"), val = bool(false)]; + tensor module_flow_span_segment_embed_weight_to_fp16 = const()[name = string("module_flow_span_segment_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33792)))]; + string input_3_to_uint16_dtype_0 = const()[name = string("input_3_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_3_to_uint16 = cast(dtype = input_3_to_uint16_dtype_0, x = input_3)[name = string("cast_8")]; + tensor var_72_cast_fp16_cast_uint16 = gather(axis = var_72_axis_0, batch_dims = var_72_batch_dims_0, indices = input_3_to_uint16, validate_indices = var_72_validate_indices_0, x = module_flow_span_segment_embed_weight_to_fp16)[name = string("op_72_cast_fp16_cast_uint16")]; + tensor var_73_cast_fp16 = add(x = op_70_cast_fp16_cast_uint16_cast_uint16, y = var_72_cast_fp16_cast_uint16)[name = string("op_73_cast_fp16")]; + int32 var_75_axis_0 = const()[name = string("op_75_axis_0"), val = int32(0)]; + int32 var_75_batch_dims_0 = const()[name = string("op_75_batch_dims_0"), val = int32(0)]; + bool var_75_validate_indices_0 = const()[name = string("op_75_validate_indices_0"), val = bool(false)]; + tensor module_flow_span_position_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331136))))[name = string("module_flow_span_position_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_7")]; + tensor var_75_cast_fp16_cast_uint16 = gather(axis = var_75_axis_0, batch_dims = var_75_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_75_validate_indices_0, x = module_flow_span_position_embed_weight_to_fp16_quantized)[name = string("op_75_cast_fp16_cast_uint16")]; + tensor hidden_1_cast_fp16 = add(x = var_73_cast_fp16, y = var_75_cast_fp16_cast_uint16)[name = string("hidden_1_cast_fp16")]; + tensor var_82_axes_0 = const()[name = string("op_82_axes_0"), val = tensor([-1])]; + tensor var_82 = expand_dims(axes = var_82_axes_0, x = span_context_mask_1)[name = string("op_82")]; + tensor var_83 = logical_not(x = var_82)[name = string("op_83")]; + fp16 var_18_to_fp16 = const()[name = string("op_18_to_fp16"), val = fp16(0x0p+0)]; + tensor hidden_3_cast_fp16 = select(a = var_18_to_fp16, b = hidden_1_cast_fp16, cond = var_83)[name = string("hidden_3_cast_fp16")]; + tensor input_7_axes_0 = const()[name = string("input_7_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332736)))]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333568)))]; + fp16 var_28_to_fp16 = const()[name = string("op_28_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16, x = hidden_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(776832))))[name = string("module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(779200)))]; + tensor linear_0_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_100 = const()[name = string("op_100"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_100, x = linear_0_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_102_split_sizes_0 = const()[name = string("op_102_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_102_axis_0 = const()[name = string("op_102_axis_0"), val = int32(2)]; + tensor var_102_cast_fp16_0, tensor var_102_cast_fp16_1, tensor var_102_cast_fp16_2 = split(axis = var_102_axis_0, split_sizes = var_102_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_102_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_102_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_102_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_102_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_110_transpose_x_0 = const()[name = string("op_110_transpose_x_0"), val = bool(false)]; + bool var_110_transpose_y_0 = const()[name = string("op_110_transpose_y_0"), val = bool(false)]; + tensor transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_9_perm_0 = const()[name = string("transpose_9_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_9 = transpose(perm = transpose_9_perm_0, x = squeeze_1_cast_fp16)[name = string("transpose_18")]; + tensor transpose_8 = transpose(perm = transpose_8_perm_0, x = squeeze_0_cast_fp16)[name = string("transpose_19")]; + tensor var_110_cast_fp16 = matmul(transpose_x = var_110_transpose_x_0, transpose_y = var_110_transpose_y_0, x = transpose_8, y = transpose_9)[name = string("op_110_cast_fp16")]; + fp16 var_111_to_fp16 = const()[name = string("op_111_to_fp16"), val = fp16(0x1p-3)]; + tensor scores_1_cast_fp16 = mul(x = var_110_cast_fp16, y = var_111_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor var_114_axes_0 = const()[name = string("op_114_axes_0"), val = tensor([1])]; + tensor var_114 = expand_dims(axes = var_114_axes_0, x = span_context_mask_1)[name = string("op_114")]; + tensor var_115_axes_0 = const()[name = string("op_115_axes_0"), val = tensor([2])]; + tensor var_115 = expand_dims(axes = var_115_axes_0, x = var_114)[name = string("op_115")]; + tensor var_117 = logical_not(x = var_115)[name = string("op_117")]; + fp16 var_22_to_fp16 = const()[name = string("op_22_to_fp16"), val = fp16(-inf)]; + tensor scores_3_cast_fp16 = select(a = var_22_to_fp16, b = scores_1_cast_fp16, cond = var_117)[name = string("scores_3_cast_fp16")]; + tensor var_119_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = string("op_119_cast_fp16")]; + string var_120_to_fp16_dtype_0 = const()[name = string("op_120_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_115_to_fp16 = cast(dtype = var_120_to_fp16_dtype_0, x = var_115)[name = string("cast_6")]; + tensor weights_1_cast_fp16 = mul(x = var_119_cast_fp16, y = var_115_to_fp16)[name = string("weights_1_cast_fp16")]; + tensor var_123_axes_0 = const()[name = string("op_123_axes_0"), val = tensor([-1])]; + bool var_123_keep_dims_0 = const()[name = string("op_123_keep_dims_0"), val = bool(true)]; + tensor var_123_cast_fp16 = reduce_sum(axes = var_123_axes_0, keep_dims = var_123_keep_dims_0, x = weights_1_cast_fp16)[name = string("op_123_cast_fp16")]; + fp16 var_21_to_fp16 = const()[name = string("op_21_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_124_cast_fp16 = maximum(x = var_123_cast_fp16, y = var_21_to_fp16)[name = string("op_124_cast_fp16")]; + tensor weights_3_cast_fp16 = real_div(x = weights_1_cast_fp16, y = var_124_cast_fp16)[name = string("weights_3_cast_fp16")]; + bool var_126_transpose_x_0 = const()[name = string("op_126_transpose_x_0"), val = bool(false)]; + bool var_126_transpose_y_0 = const()[name = string("op_126_transpose_y_0"), val = bool(false)]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_17")]; + tensor var_126_cast_fp16 = matmul(transpose_x = var_126_transpose_x_0, transpose_y = var_126_transpose_y_0, x = weights_3_cast_fp16, y = value_3_cast_fp16)[name = string("op_126_cast_fp16")]; + tensor var_127_perm_0 = const()[name = string("op_127_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_128 = const()[name = string("op_128"), val = tensor([1, 512, 384])]; + tensor var_127_cast_fp16 = transpose(perm = var_127_perm_0, x = var_126_cast_fp16)[name = string("transpose_16")]; + tensor input_9_cast_fp16 = reshape(shape = var_128, x = var_127_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(781568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(929088))))[name = string("module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(929920)))]; + tensor linear_1_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = hidden_3_cast_fp16, y = linear_1_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(930752)))]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(931584)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(932416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1522304))))[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1525440)))]; + tensor linear_2_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_19_cast_fp16 = gelu(mode = input_19_mode_0, x = linear_2_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2118464))))[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2119296)))]; + tensor linear_3_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor hidden_5_cast_fp16 = add(x = input_13_cast_fp16, y = linear_3_cast_fp16)[name = string("hidden_5_cast_fp16")]; + tensor hidden_7_cast_fp16 = select(a = var_18_to_fp16, b = hidden_5_cast_fp16, cond = var_83)[name = string("hidden_7_cast_fp16")]; + tensor input_23_axes_0 = const()[name = string("input_23_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2120128)))]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2120960)))]; + tensor input_23_cast_fp16 = layer_norm(axes = input_23_axes_0, beta = module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16, x = hidden_7_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2121792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564224))))[name = string("module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566592)))]; + tensor linear_4_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_168 = const()[name = string("op_168"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_168, x = linear_4_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_170_split_sizes_0 = const()[name = string("op_170_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(2)]; + tensor var_170_cast_fp16_0, tensor var_170_cast_fp16_1, tensor var_170_cast_fp16_2 = split(axis = var_170_axis_0, split_sizes = var_170_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_170_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_170_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_170_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_170_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_178_transpose_x_0 = const()[name = string("op_178_transpose_x_0"), val = bool(false)]; + bool var_178_transpose_y_0 = const()[name = string("op_178_transpose_y_0"), val = bool(false)]; + tensor transpose_10_perm_0 = const()[name = string("transpose_10_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_11_perm_0 = const()[name = string("transpose_11_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_11 = transpose(perm = transpose_11_perm_0, x = squeeze_4_cast_fp16)[name = string("transpose_14")]; + tensor transpose_10 = transpose(perm = transpose_10_perm_0, x = squeeze_3_cast_fp16)[name = string("transpose_15")]; + tensor var_178_cast_fp16 = matmul(transpose_x = var_178_transpose_x_0, transpose_y = var_178_transpose_y_0, x = transpose_10, y = transpose_11)[name = string("op_178_cast_fp16")]; + fp16 var_179_to_fp16 = const()[name = string("op_179_to_fp16"), val = fp16(0x1p-3)]; + tensor scores_5_cast_fp16 = mul(x = var_178_cast_fp16, y = var_179_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_22_to_fp16, b = scores_5_cast_fp16, cond = var_117)[name = string("scores_cast_fp16")]; + tensor var_187_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = string("op_187_cast_fp16")]; + tensor weights_5_cast_fp16 = mul(x = var_187_cast_fp16, y = var_115_to_fp16)[name = string("weights_5_cast_fp16")]; + tensor var_191_axes_0 = const()[name = string("op_191_axes_0"), val = tensor([-1])]; + bool var_191_keep_dims_0 = const()[name = string("op_191_keep_dims_0"), val = bool(true)]; + tensor var_191_cast_fp16 = reduce_sum(axes = var_191_axes_0, keep_dims = var_191_keep_dims_0, x = weights_5_cast_fp16)[name = string("op_191_cast_fp16")]; + tensor var_192_cast_fp16 = maximum(x = var_191_cast_fp16, y = var_21_to_fp16)[name = string("op_192_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_5_cast_fp16, y = var_192_cast_fp16)[name = string("weights_cast_fp16")]; + bool var_194_transpose_x_0 = const()[name = string("op_194_transpose_x_0"), val = bool(false)]; + bool var_194_transpose_y_0 = const()[name = string("op_194_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_13")]; + tensor var_194_cast_fp16 = matmul(transpose_x = var_194_transpose_x_0, transpose_y = var_194_transpose_y_0, x = weights_cast_fp16, y = value_cast_fp16)[name = string("op_194_cast_fp16")]; + tensor var_195_perm_0 = const()[name = string("op_195_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_196 = const()[name = string("op_196"), val = tensor([1, 512, 384])]; + tensor var_195_cast_fp16 = transpose(perm = var_195_perm_0, x = var_194_cast_fp16)[name = string("transpose_12")]; + tensor input_25_cast_fp16 = reshape(shape = var_196, x = var_195_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2568960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2716480))))[name = string("module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2717312)))]; + tensor linear_5_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_5_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor input_31_axes_0 = const()[name = string("input_31_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2718144)))]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2718976)))]; + tensor input_31_cast_fp16 = layer_norm(axes = input_31_axes_0, beta = module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2719808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3309696))))[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3312832)))]; + tensor linear_6_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_6_cast_fp16")]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_35_cast_fp16 = gelu(mode = input_35_mode_0, x = linear_6_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3315968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3905856))))[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3906688)))]; + tensor linear_7_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor hidden_9_cast_fp16 = add(x = input_29_cast_fp16, y = linear_7_cast_fp16)[name = string("hidden_9_cast_fp16")]; + tensor hidden_cast_fp16 = select(a = var_18_to_fp16, b = hidden_9_cast_fp16, cond = var_83)[name = string("hidden_cast_fp16")]; + tensor var_224 = equal(x = input_3, y = var_40)[name = string("op_224")]; + tensor var_225 = logical_and(x = span_context_mask_1, y = var_224)[name = string("op_225")]; + tensor var_228_axes_0 = const()[name = string("op_228_axes_0"), val = tensor([1])]; + bool var_228_keep_dims_0 = const()[name = string("op_228_keep_dims_0"), val = bool(true)]; + string weight_15_to_fp16_dtype_0 = const()[name = string("weight_15_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_225_to_fp16 = cast(dtype = weight_15_to_fp16_dtype_0, x = var_225)[name = string("cast_5")]; + tensor var_228_cast_fp16 = reduce_sum(axes = var_228_axes_0, keep_dims = var_228_keep_dims_0, x = var_225_to_fp16)[name = string("op_228_cast_fp16")]; + fp16 var_17_to_fp16 = const()[name = string("op_17_to_fp16"), val = fp16(0x1p+0)]; + tensor var_229_cast_fp16 = maximum(x = var_228_cast_fp16, y = var_17_to_fp16)[name = string("op_229_cast_fp16")]; + tensor weight_17_cast_fp16 = real_div(x = var_225_to_fp16, y = var_229_cast_fp16)[name = string("weight_17_cast_fp16")]; + tensor var_231_axes_0 = const()[name = string("op_231_axes_0"), val = tensor([-1])]; + tensor var_231_cast_fp16 = expand_dims(axes = var_231_axes_0, x = weight_17_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_cast_fp16 = mul(x = hidden_cast_fp16, y = var_231_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_234_axes_0 = const()[name = string("op_234_axes_0"), val = tensor([1])]; + bool var_234_keep_dims_0 = const()[name = string("op_234_keep_dims_0"), val = bool(false)]; + tensor var_234_cast_fp16 = reduce_sum(axes = var_234_axes_0, keep_dims = var_234_keep_dims_0, x = var_232_cast_fp16)[name = string("op_234_cast_fp16")]; + tensor var_235 = equal(x = input_3, y = var_38)[name = string("op_235")]; + tensor var_236 = logical_and(x = span_context_mask_1, y = var_235)[name = string("op_236")]; + tensor var_239_axes_0 = const()[name = string("op_239_axes_0"), val = tensor([1])]; + bool var_239_keep_dims_0 = const()[name = string("op_239_keep_dims_0"), val = bool(true)]; + string weight_19_to_fp16_dtype_0 = const()[name = string("weight_19_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_236_to_fp16 = cast(dtype = weight_19_to_fp16_dtype_0, x = var_236)[name = string("cast_4")]; + tensor var_239_cast_fp16 = reduce_sum(axes = var_239_axes_0, keep_dims = var_239_keep_dims_0, x = var_236_to_fp16)[name = string("op_239_cast_fp16")]; + tensor var_240_cast_fp16 = maximum(x = var_239_cast_fp16, y = var_17_to_fp16)[name = string("op_240_cast_fp16")]; + tensor weight_21_cast_fp16 = real_div(x = var_236_to_fp16, y = var_240_cast_fp16)[name = string("weight_21_cast_fp16")]; + tensor var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor([-1])]; + tensor var_242_cast_fp16 = expand_dims(axes = var_242_axes_0, x = weight_21_cast_fp16)[name = string("op_242_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = hidden_cast_fp16, y = var_242_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor var_245_axes_0 = const()[name = string("op_245_axes_0"), val = tensor([1])]; + bool var_245_keep_dims_0 = const()[name = string("op_245_keep_dims_0"), val = bool(false)]; + tensor var_245_cast_fp16 = reduce_sum(axes = var_245_axes_0, keep_dims = var_245_keep_dims_0, x = var_243_cast_fp16)[name = string("op_245_cast_fp16")]; + tensor var_246 = equal(x = input_3, y = var_35)[name = string("op_246")]; + tensor var_247 = logical_and(x = span_context_mask_1, y = var_246)[name = string("op_247")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([1])]; + bool var_250_keep_dims_0 = const()[name = string("op_250_keep_dims_0"), val = bool(true)]; + string weight_23_to_fp16_dtype_0 = const()[name = string("weight_23_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_247_to_fp16 = cast(dtype = weight_23_to_fp16_dtype_0, x = var_247)[name = string("cast_3")]; + tensor var_250_cast_fp16 = reduce_sum(axes = var_250_axes_0, keep_dims = var_250_keep_dims_0, x = var_247_to_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = maximum(x = var_250_cast_fp16, y = var_17_to_fp16)[name = string("op_251_cast_fp16")]; + tensor weight_25_cast_fp16 = real_div(x = var_247_to_fp16, y = var_251_cast_fp16)[name = string("weight_25_cast_fp16")]; + tensor var_253_axes_0 = const()[name = string("op_253_axes_0"), val = tensor([-1])]; + tensor var_253_cast_fp16 = expand_dims(axes = var_253_axes_0, x = weight_25_cast_fp16)[name = string("op_253_cast_fp16")]; + tensor var_254_cast_fp16 = mul(x = hidden_cast_fp16, y = var_253_cast_fp16)[name = string("op_254_cast_fp16")]; + tensor var_256_axes_0 = const()[name = string("op_256_axes_0"), val = tensor([1])]; + bool var_256_keep_dims_0 = const()[name = string("op_256_keep_dims_0"), val = bool(false)]; + tensor var_256_cast_fp16 = reduce_sum(axes = var_256_axes_0, keep_dims = var_256_keep_dims_0, x = var_254_cast_fp16)[name = string("op_256_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_30, interleave = input_39_interleave_0, values = (var_234_cast_fp16, var_245_cast_fp16, var_256_cast_fp16))[name = string("input_39_cast_fp16")]; + tensor module_flow_span_segment_pool_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3907520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4349952))))[name = string("module_flow_span_segment_pool_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_segment_pool_proj_bias_to_fp16 = const()[name = string("module_flow_span_segment_pool_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4350784)))]; + tensor linear_8_cast_fp16 = linear(bias = module_flow_span_segment_pool_proj_bias_to_fp16, weight = module_flow_span_segment_pool_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor pooled_axes_0 = const()[name = string("pooled_axes_0"), val = tensor([-1])]; + tensor module_flow_span_context_norm_weight_to_fp16 = const()[name = string("module_flow_span_context_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4351616)))]; + tensor module_flow_span_context_norm_bias_to_fp16 = const()[name = string("module_flow_span_context_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4352448)))]; + tensor pooled_cast_fp16 = layer_norm(axes = pooled_axes_0, beta = module_flow_span_context_norm_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_context_norm_weight_to_fp16, x = linear_8_cast_fp16)[name = string("pooled_cast_fp16")]; + tensor var_38_list = const()[name = string("op_38_list"), val = tensor([1])]; + bool reduce_max_0_keep_dims_0 = const()[name = string("reduce_max_0_keep_dims_0"), val = bool(true)]; + tensor reduce_max_0 = reduce_max(axes = var_38_list, keep_dims = reduce_max_0_keep_dims_0, x = var_64)[name = string("reduce_max_0")]; + string var_266_dtype_0 = const()[name = string("op_266_dtype_0"), val = string("bool")]; + tensor var_266 = cast(dtype = var_266_dtype_0, x = reduce_max_0)[name = string("cast_2")]; + tensor var_267 = logical_not(x = var_266)[name = string("op_267")]; + tensor var_268_cast_fp16 = select(a = var_18_to_fp16, b = pooled_cast_fp16, cond = var_267)[name = string("op_268_cast_fp16")]; + string var_268_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_268_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor span_context_hidden = cast(dtype = var_268_cast_fp16_to_fp32_dtype_0, x = var_268_cast_fp16)[name = string("cast_1")]; + } -> (span_context_hidden); +} \ No newline at end of file diff --git a/coreml/coreml/pitch_context_encoder.mlmodelc/weights/weight.bin b/coreml/coreml/pitch_context_encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c1bff3741b46a60bcef91dee85fdfa7b00f2d53c --- /dev/null +++ b/coreml/coreml/pitch_context_encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:240f333dbe5c13044acae1642fb7985ea0db3f0495874851bd78228f4c3cd334 +size 4353280 diff --git a/coreml/coreml/pitch_estimator.mlmodelc/analytics/coremldata.bin b/coreml/coreml/pitch_estimator.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2626814b54cf54b1976636e44d32967d922b00f3 --- /dev/null +++ b/coreml/coreml/pitch_estimator.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4fa8be0476ebae87fe524d6e640a2563e6807d226f14ca8b3f63faebfe566f5 +size 243 diff --git a/coreml/coreml/pitch_estimator.mlmodelc/coremldata.bin b/coreml/coreml/pitch_estimator.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..4a4dc2a29886272ee3428285cc155e6836c5070d --- /dev/null +++ b/coreml/coreml/pitch_estimator.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:afeb64f3c064a15518f25027ab0ebd28b09f8768ac8a0f70df65f542f0bd8bdf +size 3292 diff --git a/coreml/coreml/pitch_estimator.mlmodelc/model.mil b/coreml/coreml/pitch_estimator.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..245cf9aca53a15363ae34bc59936b72d3689f31b --- /dev/null +++ b/coreml/coreml/pitch_estimator.mlmodelc/model.mil @@ -0,0 +1,5758 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func pitch_estimator(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23532544))), scale = tensor([[[0x1.3a8p-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 1024])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925824)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 1024, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 1024, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 1024, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 1024, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 1024, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 1024, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 1024, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 1024, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.e18p-7, 0x1.da8p-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_128(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171392))), scale = tensor([[[0x1.17p-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 128])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254592)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 128, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 128, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 128, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 128, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 128, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 128, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 128, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 128, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.e18p-7, 0x1.da8p-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_256(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22790912))), scale = tensor([[[0x1.35cp-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 256])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22889280)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 256, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 256, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 256, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 256, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 256, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 256, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 256, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 256, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.e18p-7, 0x1.da8p-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_384(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22889856))), scale = tensor([[[0x1.35cp-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 384])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23037376)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 384, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 384, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 384, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 384, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 384, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 384, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 384, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 384, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.e18p-7, 0x1.da8p-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_512(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23038208))), scale = tensor([[[0x1.3a8p-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 512])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23234880)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 512, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 512, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 512, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 512, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 512, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 512, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 512, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 512, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.e18p-7, 0x1.da8p-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_768(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23235968))), scale = tensor([[[0x1.3a8p-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 768])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23530944)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 768, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 768, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 768, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 768, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 768, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 768, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 768, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 768, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.e18p-7, 0x1.da8p-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); +} \ No newline at end of file diff --git a/coreml/coreml/pitch_estimator.mlmodelc/weights/weight.bin b/coreml/coreml/pitch_estimator.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..cafc8d81d79cd0e036a16fb0f40ae1b08d422f5e --- /dev/null +++ b/coreml/coreml/pitch_estimator.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1df3ce395d51695966dfca278714e6aa9a5641a4e2c2c5c8c2a3defbbf12683f +size 23927936 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a3352656a3dcf3ea5e483ac534d3cf5c3dab871e --- /dev/null +++ b/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f5a434cb6dc9d6137bc8a5c26bf7b49716c35a4e6b252b104f03a0fb5b53c2d +size 243 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin b/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..f0da457e72624d38d2d3a6bd34b4e6dd60c3c373 --- /dev/null +++ b/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2831a5db7df542f88d581caec0b3a1091cd6e2522c0a7c82da21357327719517 +size 476 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/model.mil b/coreml/coreml/vector_context_encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..ad2811e8901c10ff15b8f7c7ff21f24fde90f076 --- /dev/null +++ b/coreml/coreml/vector_context_encoder.mlmodelc/model.mil @@ -0,0 +1,267 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor span_context_mask, tensor span_context_phone_ids, tensor span_context_segment_ids) { + int32 var_15 = const()[name = string("op_15"), val = int32(0)]; + tensor span_context_mask_1 = not_equal(x = span_context_mask, y = var_15)[name = string("span_context_mask")]; + int32 var_30 = const()[name = string("op_30"), val = int32(-1)]; + int32 var_35 = const()[name = string("op_35"), val = int32(2)]; + int32 var_36 = const()[name = string("op_36"), val = int32(86)]; + int32 var_38 = const()[name = string("op_38"), val = int32(1)]; + int32 var_40 = const()[name = string("op_40"), val = int32(0)]; + tensor var_55 = maximum(x = span_context_phone_ids, y = var_40)[name = string("op_55")]; + tensor input_1 = minimum(x = var_55, y = var_36)[name = string("input_1")]; + tensor var_59 = maximum(x = span_context_segment_ids, y = var_40)[name = string("op_59")]; + tensor input_3 = minimum(x = var_59, y = var_35)[name = string("input_3")]; + string var_64_dtype_0 = const()[name = string("op_64_dtype_0"), val = string("int32")]; + bool var_65_exclusive_0 = const()[name = string("op_65_exclusive_0"), val = bool(false)]; + bool var_65_reverse_0 = const()[name = string("op_65_reverse_0"), val = bool(false)]; + tensor var_64 = cast(dtype = var_64_dtype_0, x = span_context_mask_1)[name = string("cast_13")]; + tensor var_65 = cumsum(axis = var_38, exclusive = var_65_exclusive_0, reverse = var_65_reverse_0, x = var_64)[name = string("op_65")]; + int32 var_66 = const()[name = string("op_66"), val = int32(1)]; + tensor var_67 = sub(x = var_65, y = var_66)[name = string("op_67")]; + tensor input_5 = maximum(x = var_67, y = var_40)[name = string("input_5")]; + int32 var_70_batch_dims_0 = const()[name = string("op_70_batch_dims_0"), val = int32(0)]; + bool var_70_validate_indices_0 = const()[name = string("op_70_validate_indices_0"), val = bool(false)]; + tensor module_flow_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44672))))[name = string("module_flow_phone_embed_weight_to_fp16_quantized")]; + string input_1_to_int16_dtype_0 = const()[name = string("input_1_to_int16_dtype_0"), val = string("int16")]; + string cast_24_dtype_0 = const()[name = string("cast_24_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor input_1_to_int16 = cast(dtype = input_1_to_int16_dtype_0, x = input_1)[name = string("cast_12")]; + tensor cast_24 = cast(dtype = cast_24_dtype_0, x = input_1_to_int16)[name = string("cast_11")]; + tensor greater_equal_0 = greater_equal(x = cast_24, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(87)]; + tensor add_0 = add(x = cast_24, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_24, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_10")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_9")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(87)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_70_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_70_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_70_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_70_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_70_batch_dims_0, indices = select_0_1, validate_indices = var_70_validate_indices_0, x = module_flow_phone_embed_weight_to_fp16_quantized)[name = string("op_70_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_72_axis_0 = const()[name = string("op_72_axis_0"), val = int32(0)]; + int32 var_72_batch_dims_0 = const()[name = string("op_72_batch_dims_0"), val = int32(0)]; + bool var_72_validate_indices_0 = const()[name = string("op_72_validate_indices_0"), val = bool(false)]; + tensor module_flow_span_segment_embed_weight_to_fp16 = const()[name = string("module_flow_span_segment_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44928)))]; + string input_3_to_uint16_dtype_0 = const()[name = string("input_3_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_3_to_uint16 = cast(dtype = input_3_to_uint16_dtype_0, x = input_3)[name = string("cast_8")]; + tensor var_72_cast_fp16_cast_uint16 = gather(axis = var_72_axis_0, batch_dims = var_72_batch_dims_0, indices = input_3_to_uint16, validate_indices = var_72_validate_indices_0, x = module_flow_span_segment_embed_weight_to_fp16)[name = string("op_72_cast_fp16_cast_uint16")]; + tensor var_73_cast_fp16 = add(x = op_70_cast_fp16_cast_uint16_cast_uint16, y = var_72_cast_fp16_cast_uint16)[name = string("op_73_cast_fp16")]; + int32 var_75_axis_0 = const()[name = string("op_75_axis_0"), val = int32(0)]; + int32 var_75_batch_dims_0 = const()[name = string("op_75_batch_dims_0"), val = int32(0)]; + bool var_75_validate_indices_0 = const()[name = string("op_75_validate_indices_0"), val = bool(false)]; + tensor module_flow_span_position_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441344))))[name = string("module_flow_span_position_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_7")]; + tensor var_75_cast_fp16_cast_uint16 = gather(axis = var_75_axis_0, batch_dims = var_75_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_75_validate_indices_0, x = module_flow_span_position_embed_weight_to_fp16_quantized)[name = string("op_75_cast_fp16_cast_uint16")]; + tensor hidden_1_cast_fp16 = add(x = var_73_cast_fp16, y = var_75_cast_fp16_cast_uint16)[name = string("hidden_1_cast_fp16")]; + tensor var_82_axes_0 = const()[name = string("op_82_axes_0"), val = tensor([-1])]; + tensor var_82 = expand_dims(axes = var_82_axes_0, x = span_context_mask_1)[name = string("op_82")]; + tensor var_83 = logical_not(x = var_82)[name = string("op_83")]; + fp16 var_18_to_fp16 = const()[name = string("op_18_to_fp16"), val = fp16(0x0p+0)]; + tensor hidden_3_cast_fp16 = select(a = var_18_to_fp16, b = hidden_1_cast_fp16, cond = var_83)[name = string("hidden_3_cast_fp16")]; + tensor input_7_axes_0 = const()[name = string("input_7_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442944)))]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444032)))]; + fp16 var_28_to_fp16 = const()[name = string("op_28_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16, x = hidden_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1231616))))[name = string("module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1234752)))]; + tensor linear_0_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_100 = const()[name = string("op_100"), val = tensor([1, 512, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_100, x = linear_0_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_102_split_sizes_0 = const()[name = string("op_102_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_102_axis_0 = const()[name = string("op_102_axis_0"), val = int32(2)]; + tensor var_102_cast_fp16_0, tensor var_102_cast_fp16_1, tensor var_102_cast_fp16_2 = split(axis = var_102_axis_0, split_sizes = var_102_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_102_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_102_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_102_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_102_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_110_transpose_x_0 = const()[name = string("op_110_transpose_x_0"), val = bool(false)]; + bool var_110_transpose_y_0 = const()[name = string("op_110_transpose_y_0"), val = bool(false)]; + tensor transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_9_perm_0 = const()[name = string("transpose_9_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_9 = transpose(perm = transpose_9_perm_0, x = squeeze_1_cast_fp16)[name = string("transpose_18")]; + tensor transpose_8 = transpose(perm = transpose_8_perm_0, x = squeeze_0_cast_fp16)[name = string("transpose_19")]; + tensor var_110_cast_fp16 = matmul(transpose_x = var_110_transpose_x_0, transpose_y = var_110_transpose_y_0, x = transpose_8, y = transpose_9)[name = string("op_110_cast_fp16")]; + fp16 var_111_to_fp16 = const()[name = string("op_111_to_fp16"), val = fp16(0x1p-3)]; + tensor scores_1_cast_fp16 = mul(x = var_110_cast_fp16, y = var_111_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor var_114_axes_0 = const()[name = string("op_114_axes_0"), val = tensor([1])]; + tensor var_114 = expand_dims(axes = var_114_axes_0, x = span_context_mask_1)[name = string("op_114")]; + tensor var_115_axes_0 = const()[name = string("op_115_axes_0"), val = tensor([2])]; + tensor var_115 = expand_dims(axes = var_115_axes_0, x = var_114)[name = string("op_115")]; + tensor var_117 = logical_not(x = var_115)[name = string("op_117")]; + fp16 var_23_to_fp16 = const()[name = string("op_23_to_fp16"), val = fp16(-inf)]; + tensor scores_3_cast_fp16 = select(a = var_23_to_fp16, b = scores_1_cast_fp16, cond = var_117)[name = string("scores_3_cast_fp16")]; + tensor var_119_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = string("op_119_cast_fp16")]; + string var_120_to_fp16_dtype_0 = const()[name = string("op_120_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_115_to_fp16 = cast(dtype = var_120_to_fp16_dtype_0, x = var_115)[name = string("cast_6")]; + tensor weights_1_cast_fp16 = mul(x = var_119_cast_fp16, y = var_115_to_fp16)[name = string("weights_1_cast_fp16")]; + tensor var_123_axes_0 = const()[name = string("op_123_axes_0"), val = tensor([-1])]; + bool var_123_keep_dims_0 = const()[name = string("op_123_keep_dims_0"), val = bool(true)]; + tensor var_123_cast_fp16 = reduce_sum(axes = var_123_axes_0, keep_dims = var_123_keep_dims_0, x = weights_1_cast_fp16)[name = string("op_123_cast_fp16")]; + fp16 var_21_to_fp16 = const()[name = string("op_21_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_124_cast_fp16 = maximum(x = var_123_cast_fp16, y = var_21_to_fp16)[name = string("op_124_cast_fp16")]; + tensor weights_3_cast_fp16 = real_div(x = weights_1_cast_fp16, y = var_124_cast_fp16)[name = string("weights_3_cast_fp16")]; + bool var_126_transpose_x_0 = const()[name = string("op_126_transpose_x_0"), val = bool(false)]; + bool var_126_transpose_y_0 = const()[name = string("op_126_transpose_y_0"), val = bool(false)]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_17")]; + tensor var_126_cast_fp16 = matmul(transpose_x = var_126_transpose_x_0, transpose_y = var_126_transpose_y_0, x = weights_3_cast_fp16, y = value_3_cast_fp16)[name = string("op_126_cast_fp16")]; + tensor var_127_perm_0 = const()[name = string("op_127_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_128 = const()[name = string("op_128"), val = tensor([1, 512, 512])]; + tensor var_127_cast_fp16 = transpose(perm = var_127_perm_0, x = var_126_cast_fp16)[name = string("transpose_16")]; + tensor input_9_cast_fp16 = reshape(shape = var_128, x = var_127_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1237888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1500096))))[name = string("module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1501184)))]; + tensor linear_1_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = hidden_3_cast_fp16, y = linear_1_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1502272)))]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1503360)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1504448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2553088))))[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2557248)))]; + tensor linear_2_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_19_cast_fp16 = gelu(mode = input_19_mode_0, x = linear_2_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2561408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3610048))))[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3611136)))]; + tensor linear_3_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor hidden_5_cast_fp16 = add(x = input_13_cast_fp16, y = linear_3_cast_fp16)[name = string("hidden_5_cast_fp16")]; + tensor hidden_7_cast_fp16 = select(a = var_18_to_fp16, b = hidden_5_cast_fp16, cond = var_83)[name = string("hidden_7_cast_fp16")]; + tensor input_23_axes_0 = const()[name = string("input_23_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3612224)))]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3613312)))]; + tensor input_23_cast_fp16 = layer_norm(axes = input_23_axes_0, beta = module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16, x = hidden_7_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3614400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4400896))))[name = string("module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4404032)))]; + tensor linear_4_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_168 = const()[name = string("op_168"), val = tensor([1, 512, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_168, x = linear_4_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_170_split_sizes_0 = const()[name = string("op_170_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(2)]; + tensor var_170_cast_fp16_0, tensor var_170_cast_fp16_1, tensor var_170_cast_fp16_2 = split(axis = var_170_axis_0, split_sizes = var_170_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_170_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_170_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_170_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_170_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_178_transpose_x_0 = const()[name = string("op_178_transpose_x_0"), val = bool(false)]; + bool var_178_transpose_y_0 = const()[name = string("op_178_transpose_y_0"), val = bool(false)]; + tensor transpose_10_perm_0 = const()[name = string("transpose_10_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_11_perm_0 = const()[name = string("transpose_11_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_11 = transpose(perm = transpose_11_perm_0, x = squeeze_4_cast_fp16)[name = string("transpose_14")]; + tensor transpose_10 = transpose(perm = transpose_10_perm_0, x = squeeze_3_cast_fp16)[name = string("transpose_15")]; + tensor var_178_cast_fp16 = matmul(transpose_x = var_178_transpose_x_0, transpose_y = var_178_transpose_y_0, x = transpose_10, y = transpose_11)[name = string("op_178_cast_fp16")]; + fp16 var_179_to_fp16 = const()[name = string("op_179_to_fp16"), val = fp16(0x1p-3)]; + tensor scores_5_cast_fp16 = mul(x = var_178_cast_fp16, y = var_179_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_23_to_fp16, b = scores_5_cast_fp16, cond = var_117)[name = string("scores_cast_fp16")]; + tensor var_187_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = string("op_187_cast_fp16")]; + tensor weights_5_cast_fp16 = mul(x = var_187_cast_fp16, y = var_115_to_fp16)[name = string("weights_5_cast_fp16")]; + tensor var_191_axes_0 = const()[name = string("op_191_axes_0"), val = tensor([-1])]; + bool var_191_keep_dims_0 = const()[name = string("op_191_keep_dims_0"), val = bool(true)]; + tensor var_191_cast_fp16 = reduce_sum(axes = var_191_axes_0, keep_dims = var_191_keep_dims_0, x = weights_5_cast_fp16)[name = string("op_191_cast_fp16")]; + tensor var_192_cast_fp16 = maximum(x = var_191_cast_fp16, y = var_21_to_fp16)[name = string("op_192_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_5_cast_fp16, y = var_192_cast_fp16)[name = string("weights_cast_fp16")]; + bool var_194_transpose_x_0 = const()[name = string("op_194_transpose_x_0"), val = bool(false)]; + bool var_194_transpose_y_0 = const()[name = string("op_194_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_13")]; + tensor var_194_cast_fp16 = matmul(transpose_x = var_194_transpose_x_0, transpose_y = var_194_transpose_y_0, x = weights_cast_fp16, y = value_cast_fp16)[name = string("op_194_cast_fp16")]; + tensor var_195_perm_0 = const()[name = string("op_195_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_196 = const()[name = string("op_196"), val = tensor([1, 512, 512])]; + tensor var_195_cast_fp16 = transpose(perm = var_195_perm_0, x = var_194_cast_fp16)[name = string("transpose_12")]; + tensor input_25_cast_fp16 = reshape(shape = var_196, x = var_195_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4407168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4669376))))[name = string("module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4670464)))]; + tensor linear_5_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_5_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor input_31_axes_0 = const()[name = string("input_31_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4671552)))]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4672640)))]; + tensor input_31_cast_fp16 = layer_norm(axes = input_31_axes_0, beta = module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4673728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5722368))))[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5726528)))]; + tensor linear_6_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_6_cast_fp16")]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_35_cast_fp16 = gelu(mode = input_35_mode_0, x = linear_6_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5730688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6779328))))[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6780416)))]; + tensor linear_7_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor hidden_9_cast_fp16 = add(x = input_29_cast_fp16, y = linear_7_cast_fp16)[name = string("hidden_9_cast_fp16")]; + tensor hidden_cast_fp16 = select(a = var_18_to_fp16, b = hidden_9_cast_fp16, cond = var_83)[name = string("hidden_cast_fp16")]; + tensor var_224 = equal(x = input_3, y = var_40)[name = string("op_224")]; + tensor var_225 = logical_and(x = span_context_mask_1, y = var_224)[name = string("op_225")]; + tensor var_228_axes_0 = const()[name = string("op_228_axes_0"), val = tensor([1])]; + bool var_228_keep_dims_0 = const()[name = string("op_228_keep_dims_0"), val = bool(true)]; + string weight_15_to_fp16_dtype_0 = const()[name = string("weight_15_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_225_to_fp16 = cast(dtype = weight_15_to_fp16_dtype_0, x = var_225)[name = string("cast_5")]; + tensor var_228_cast_fp16 = reduce_sum(axes = var_228_axes_0, keep_dims = var_228_keep_dims_0, x = var_225_to_fp16)[name = string("op_228_cast_fp16")]; + fp16 var_17_to_fp16 = const()[name = string("op_17_to_fp16"), val = fp16(0x1p+0)]; + tensor var_229_cast_fp16 = maximum(x = var_228_cast_fp16, y = var_17_to_fp16)[name = string("op_229_cast_fp16")]; + tensor weight_17_cast_fp16 = real_div(x = var_225_to_fp16, y = var_229_cast_fp16)[name = string("weight_17_cast_fp16")]; + tensor var_231_axes_0 = const()[name = string("op_231_axes_0"), val = tensor([-1])]; + tensor var_231_cast_fp16 = expand_dims(axes = var_231_axes_0, x = weight_17_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_cast_fp16 = mul(x = hidden_cast_fp16, y = var_231_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_234_axes_0 = const()[name = string("op_234_axes_0"), val = tensor([1])]; + bool var_234_keep_dims_0 = const()[name = string("op_234_keep_dims_0"), val = bool(false)]; + tensor var_234_cast_fp16 = reduce_sum(axes = var_234_axes_0, keep_dims = var_234_keep_dims_0, x = var_232_cast_fp16)[name = string("op_234_cast_fp16")]; + tensor var_235 = equal(x = input_3, y = var_38)[name = string("op_235")]; + tensor var_236 = logical_and(x = span_context_mask_1, y = var_235)[name = string("op_236")]; + tensor var_239_axes_0 = const()[name = string("op_239_axes_0"), val = tensor([1])]; + bool var_239_keep_dims_0 = const()[name = string("op_239_keep_dims_0"), val = bool(true)]; + string weight_19_to_fp16_dtype_0 = const()[name = string("weight_19_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_236_to_fp16 = cast(dtype = weight_19_to_fp16_dtype_0, x = var_236)[name = string("cast_4")]; + tensor var_239_cast_fp16 = reduce_sum(axes = var_239_axes_0, keep_dims = var_239_keep_dims_0, x = var_236_to_fp16)[name = string("op_239_cast_fp16")]; + tensor var_240_cast_fp16 = maximum(x = var_239_cast_fp16, y = var_17_to_fp16)[name = string("op_240_cast_fp16")]; + tensor weight_21_cast_fp16 = real_div(x = var_236_to_fp16, y = var_240_cast_fp16)[name = string("weight_21_cast_fp16")]; + tensor var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor([-1])]; + tensor var_242_cast_fp16 = expand_dims(axes = var_242_axes_0, x = weight_21_cast_fp16)[name = string("op_242_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = hidden_cast_fp16, y = var_242_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor var_245_axes_0 = const()[name = string("op_245_axes_0"), val = tensor([1])]; + bool var_245_keep_dims_0 = const()[name = string("op_245_keep_dims_0"), val = bool(false)]; + tensor var_245_cast_fp16 = reduce_sum(axes = var_245_axes_0, keep_dims = var_245_keep_dims_0, x = var_243_cast_fp16)[name = string("op_245_cast_fp16")]; + tensor var_246 = equal(x = input_3, y = var_35)[name = string("op_246")]; + tensor var_247 = logical_and(x = span_context_mask_1, y = var_246)[name = string("op_247")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([1])]; + bool var_250_keep_dims_0 = const()[name = string("op_250_keep_dims_0"), val = bool(true)]; + string weight_23_to_fp16_dtype_0 = const()[name = string("weight_23_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_247_to_fp16 = cast(dtype = weight_23_to_fp16_dtype_0, x = var_247)[name = string("cast_3")]; + tensor var_250_cast_fp16 = reduce_sum(axes = var_250_axes_0, keep_dims = var_250_keep_dims_0, x = var_247_to_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = maximum(x = var_250_cast_fp16, y = var_17_to_fp16)[name = string("op_251_cast_fp16")]; + tensor weight_25_cast_fp16 = real_div(x = var_247_to_fp16, y = var_251_cast_fp16)[name = string("weight_25_cast_fp16")]; + tensor var_253_axes_0 = const()[name = string("op_253_axes_0"), val = tensor([-1])]; + tensor var_253_cast_fp16 = expand_dims(axes = var_253_axes_0, x = weight_25_cast_fp16)[name = string("op_253_cast_fp16")]; + tensor var_254_cast_fp16 = mul(x = hidden_cast_fp16, y = var_253_cast_fp16)[name = string("op_254_cast_fp16")]; + tensor var_256_axes_0 = const()[name = string("op_256_axes_0"), val = tensor([1])]; + bool var_256_keep_dims_0 = const()[name = string("op_256_keep_dims_0"), val = bool(false)]; + tensor var_256_cast_fp16 = reduce_sum(axes = var_256_axes_0, keep_dims = var_256_keep_dims_0, x = var_254_cast_fp16)[name = string("op_256_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_30, interleave = input_39_interleave_0, values = (var_234_cast_fp16, var_245_cast_fp16, var_256_cast_fp16))[name = string("input_39_cast_fp16")]; + tensor module_flow_span_segment_pool_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6781504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7568000))))[name = string("module_flow_span_segment_pool_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_segment_pool_proj_bias_to_fp16 = const()[name = string("module_flow_span_segment_pool_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7569088)))]; + tensor linear_8_cast_fp16 = linear(bias = module_flow_span_segment_pool_proj_bias_to_fp16, weight = module_flow_span_segment_pool_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor pooled_axes_0 = const()[name = string("pooled_axes_0"), val = tensor([-1])]; + tensor module_flow_span_context_norm_weight_to_fp16 = const()[name = string("module_flow_span_context_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7570176)))]; + tensor module_flow_span_context_norm_bias_to_fp16 = const()[name = string("module_flow_span_context_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7571264)))]; + tensor pooled_cast_fp16 = layer_norm(axes = pooled_axes_0, beta = module_flow_span_context_norm_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_context_norm_weight_to_fp16, x = linear_8_cast_fp16)[name = string("pooled_cast_fp16")]; + tensor var_38_list = const()[name = string("op_38_list"), val = tensor([1])]; + bool reduce_max_0_keep_dims_0 = const()[name = string("reduce_max_0_keep_dims_0"), val = bool(true)]; + tensor reduce_max_0 = reduce_max(axes = var_38_list, keep_dims = reduce_max_0_keep_dims_0, x = var_64)[name = string("reduce_max_0")]; + string var_266_dtype_0 = const()[name = string("op_266_dtype_0"), val = string("bool")]; + tensor var_266 = cast(dtype = var_266_dtype_0, x = reduce_max_0)[name = string("cast_2")]; + tensor var_267 = logical_not(x = var_266)[name = string("op_267")]; + tensor var_268_cast_fp16 = select(a = var_18_to_fp16, b = pooled_cast_fp16, cond = var_267)[name = string("op_268_cast_fp16")]; + string var_268_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_268_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor span_context_hidden = cast(dtype = var_268_cast_fp16_to_fp32_dtype_0, x = var_268_cast_fp16)[name = string("cast_1")]; + } -> (span_context_hidden); +} \ No newline at end of file diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/weights/weight.bin b/coreml/coreml/vector_context_encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ca19050731a69bcfffbe19c724d7608bfb4ac8a3 --- /dev/null +++ b/coreml/coreml/vector_context_encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f686199febbe315c2102abd521d33fb629813d2293afe9daa08c94828d14d66e +size 7572352 diff --git a/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2f5e803ba362aad6b1d9bff83c019b685430c4b7 --- /dev/null +++ b/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a60369557fe37fcd46f6245d6de4d4a55806967f5dfbca510a75e968a826882 +size 243 diff --git a/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin b/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b00101a508bac72cba29b77727afc2ae9470b549 --- /dev/null +++ b/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9b0d967ca6ab8cace2814a61e7c1561d9a45c7b65b2373825b6d53e10b9b47f +size 3845 diff --git a/coreml/coreml/vector_estimator.mlmodelc/model.mil b/coreml/coreml/vector_estimator.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..cb347e8d6154e62d88083bc7b73719f556df5a8c --- /dev/null +++ b/coreml/coreml/vector_estimator.mlmodelc/model.mil @@ -0,0 +1,8116 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func vector_estimator(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60252416))), scale = tensor([[[0x1.1fp-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 1120])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60776768)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 1120, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 1120, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 1120, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 1120, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 1120, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 1120, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 1120, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 1120, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 1120, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 1120, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 1120, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 1120, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 1120, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_128(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219264))), scale = tensor([[[0x1.1c4p-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 224])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828800)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 224, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 224, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 224, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 224, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 224, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 224, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 224, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 224, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 224, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 224, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 224, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 224, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 224, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_256(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264256))), scale = tensor([[[0x1.1c4p-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 352])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59395392)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 352, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 352, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 352, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 352, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 352, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 352, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 352, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 352, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 352, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 352, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 352, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 352, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 352, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_384(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59396160))), scale = tensor([[[0x1.1c4p-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 480])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59592832)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 480, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 480, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 480, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 480, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 480, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 480, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 480, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 480, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 480, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 480, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 480, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 480, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 480, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_512(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59593856))), scale = tensor([[[0x1.1fp-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 608])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59856064)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 608, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 608, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 608, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 608, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 608, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 608, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 608, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 608, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 608, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 608, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 608, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 608, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 608, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_768(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59857344))), scale = tensor([[[0x1.1fp-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 864])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60250624)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 864, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 864, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 864, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 864, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 864, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 864, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 864, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 864, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 864, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 864, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 864, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 864, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 864, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); +} \ No newline at end of file diff --git a/coreml/coreml/vector_estimator.mlmodelc/weights/weight.bin b/coreml/coreml/vector_estimator.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..272749d94f559d39f84508d53f308f5131f00dda --- /dev/null +++ b/coreml/coreml/vector_estimator.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b808c10f9838b2bbf671ccef3d765cebabf58243457a01c06ec11480a9392388 +size 60779072 diff --git a/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..97bfdf25dd0d873249fca0d6108075bf5c6999ac --- /dev/null +++ b/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:60f03103f36e751ddfa285184d62a93c1edc615d5fa9833e48ccc93ed577464d +size 243 diff --git a/coreml/coreml/vocoder.mlmodelc/coremldata.bin b/coreml/coreml/vocoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9c2913a225edcb7b090fb3eb1d0222641e6b3755 --- /dev/null +++ b/coreml/coreml/vocoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:04bf9f9de7edfc6ce58e5928a0a1d6669d09095ebc03cb5ca24dd85a416653df +size 1190 diff --git a/coreml/coreml/vocoder.mlmodelc/model.mil b/coreml/coreml/vocoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..39ad01dbccc9e8b3b9310af29a398b77e2a3d03e --- /dev/null +++ b/coreml/coreml/vocoder.mlmodelc/model.mil @@ -0,0 +1,5386 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func vocoder(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 var_41 = const()[name = string("op_41"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_85 = maximum(x = emotion_id, y = var_39)[name = string("op_85")]; + tensor input_5 = minimum(x = var_85, y = var_41)[name = string("input_5")]; + int32 var_88_batch_dims_0 = const()[name = string("op_88_batch_dims_0"), val = int32(0)]; + bool var_88_validate_indices_0 = const()[name = string("op_88_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_88_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_88_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_88_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_88_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_88_batch_dims_0, indices = select_0_1, validate_indices = var_88_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_88_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_90_axis_0 = const()[name = string("op_90_axis_0"), val = int32(0)]; + int32 var_90_batch_dims_0 = const()[name = string("op_90_batch_dims_0"), val = int32(0)]; + bool var_90_validate_indices_0 = const()[name = string("op_90_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_90_cast_fp16_cast_uint16 = gather(axis = var_90_axis_0, batch_dims = var_90_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_90_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_90_cast_fp16_cast_uint16")]; + tensor var_91_cast_fp16 = add(x = op_88_cast_fp16_cast_uint16_cast_uint16, y = var_90_cast_fp16_cast_uint16)[name = string("op_91_cast_fp16")]; + int32 var_93_axis_0 = const()[name = string("op_93_axis_0"), val = int32(0)]; + int32 var_93_batch_dims_0 = const()[name = string("op_93_batch_dims_0"), val = int32(0)]; + bool var_93_validate_indices_0 = const()[name = string("op_93_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_93_cast_fp16_cast_uint16 = gather(axis = var_93_axis_0, batch_dims = var_93_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_93_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_93_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_91_cast_fp16, y = var_93_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_95_axes_0 = const()[name = string("op_95_axes_0"), val = tensor([-1])]; + tensor var_95_cast_fp16 = expand_dims(axes = var_95_axes_0, x = cond_cast_fp16)[name = string("op_95_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_95_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_98_axes_0 = const()[name = string("op_98_axes_0"), val = tensor([1])]; + tensor var_98 = expand_dims(axes = var_98_axes_0, x = latent_mask_1)[name = string("op_98")]; + string var_99_to_fp16_dtype_0 = const()[name = string("op_99_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_98_to_fp16 = cast(dtype = var_99_to_fp16_dtype_0, x = var_98)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_98_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_98_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_131_axes_0 = const()[name = string("op_131_axes_0"), val = tensor([-1])]; + tensor var_131_cast_fp16 = expand_dims(axes = var_131_axes_0, x = linear_0_cast_fp16)[name = string("op_131_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_131_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_98_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_axes_0 = const()[name = string("op_155_axes_0"), val = tensor([-1])]; + tensor var_155_cast_fp16 = expand_dims(axes = var_155_axes_0, x = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_155_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_98_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_98_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_98_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_200_axes_0 = const()[name = string("op_200_axes_0"), val = tensor([-1])]; + tensor var_200_cast_fp16 = expand_dims(axes = var_200_axes_0, x = linear_2_cast_fp16)[name = string("op_200_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_200_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_98_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_224_axes_0 = const()[name = string("op_224_axes_0"), val = tensor([-1])]; + tensor var_224_cast_fp16 = expand_dims(axes = var_224_axes_0, x = linear_3_cast_fp16)[name = string("op_224_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_224_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_98_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_98_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_98_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_269_axes_0 = const()[name = string("op_269_axes_0"), val = tensor([-1])]; + tensor var_269_cast_fp16 = expand_dims(axes = var_269_axes_0, x = linear_4_cast_fp16)[name = string("op_269_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_269_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_98_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_293_axes_0 = const()[name = string("op_293_axes_0"), val = tensor([-1])]; + tensor var_293_cast_fp16 = expand_dims(axes = var_293_axes_0, x = linear_5_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_293_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_98_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_98_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_98_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([-1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = linear_6_cast_fp16)[name = string("op_338_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_338_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_98_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([-1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = linear_7_cast_fp16)[name = string("op_362_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_362_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_98_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_98_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_98_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_407_axes_0 = const()[name = string("op_407_axes_0"), val = tensor([-1])]; + tensor var_407_cast_fp16 = expand_dims(axes = var_407_axes_0, x = linear_8_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_407_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_98_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_431_axes_0 = const()[name = string("op_431_axes_0"), val = tensor([-1])]; + tensor var_431_cast_fp16 = expand_dims(axes = var_431_axes_0, x = linear_9_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_431_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_98_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_98_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_98_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + tensor var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = linear_10_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_476_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_98_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_500_axes_0 = const()[name = string("op_500_axes_0"), val = tensor([-1])]; + tensor var_500_cast_fp16 = expand_dims(axes = var_500_axes_0, x = linear_11_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_500_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_98_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_98_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 2048])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 1024, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_524 = expand_dims(axes = var_524_axes_0, x = mask_53)[name = string("op_524")]; + string var_525_to_fp16_dtype_0 = const()[name = string("op_525_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_524_to_fp16 = cast(dtype = var_525_to_fp16_dtype_0, x = var_524)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_524_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_524_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_557_axes_0 = const()[name = string("op_557_axes_0"), val = tensor([-1])]; + tensor var_557_cast_fp16 = expand_dims(axes = var_557_axes_0, x = linear_12_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_557_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_524_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_581_axes_0 = const()[name = string("op_581_axes_0"), val = tensor([-1])]; + tensor var_581_cast_fp16 = expand_dims(axes = var_581_axes_0, x = linear_13_cast_fp16)[name = string("op_581_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_581_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_524_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_524_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_524_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_637_axes_0 = const()[name = string("op_637_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_36_to_fp16 = const()[name = string("op_36_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_637_cast_fp16 = layer_norm(axes = var_637_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_637_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_637_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_663_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_663_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_661_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_663_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_702_axes_0 = const()[name = string("op_702_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_702_cast_fp16 = layer_norm(axes = var_702_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_702_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_704_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_704_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_702_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_704_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_743_axes_0 = const()[name = string("op_743_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_743_cast_fp16 = layer_norm(axes = var_743_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_743_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_745_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_745_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_743_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_745_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_784_axes_0 = const()[name = string("op_784_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_784_cast_fp16 = layer_norm(axes = var_784_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_784_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_786_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_786_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_784_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_786_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_825_cast_fp16 = layer_norm(axes = var_825_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_825_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_827_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_827_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_825_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_827_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_866_cast_fp16 = layer_norm(axes = var_866_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_868_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_868_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_866_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_868_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_907_axes_0 = const()[name = string("op_907_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_907_cast_fp16 = layer_norm(axes = var_907_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_907_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_909_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_909_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_907_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_909_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_948_axes_0 = const()[name = string("op_948_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_948_cast_fp16 = layer_norm(axes = var_948_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_948_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_950_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_950_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_948_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_950_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_978_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_978_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_978_to_fp16_quantized, x = var_977_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_984_split_sizes_0 = const()[name = string("op_984_split_sizes_0"), val = tensor([513, 513])]; + int32 var_984_axis_0 = const()[name = string("op_984_axis_0"), val = int32(1)]; + tensor var_984_cast_fp16_0, tensor var_984_cast_fp16_1 = split(axis = var_984_axis_0, split_sizes = var_984_split_sizes_0, x = values_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_986_cast_fp16 = exp(x = var_984_cast_fp16_0)[name = string("op_986_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_986_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_988_cast_fp16 = cos(x = var_984_cast_fp16_1)[name = string("op_988_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_988_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_990_cast_fp16 = sin(x = var_984_cast_fp16_1)[name = string("op_990_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_990_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1005_pad_type_0 = const()[name = string("op_1005_pad_type_0"), val = string("valid")]; + tensor var_1005_strides_0 = const()[name = string("op_1005_strides_0"), val = tensor([1])]; + tensor var_1005_pad_0 = const()[name = string("op_1005_pad_0"), val = tensor([0, 0])]; + tensor var_1005_dilations_0 = const()[name = string("op_1005_dilations_0"), val = tensor([1])]; + int32 var_1005_groups_0 = const()[name = string("op_1005_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1005_cast_fp16 = conv(dilations = var_1005_dilations_0, groups = var_1005_groups_0, pad = var_1005_pad_0, pad_type = var_1005_pad_type_0, strides = var_1005_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1005_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1005_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1009_to_fp16 = const()[name = string("op_1009_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1009_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1012 = const()[name = string("op_1012"), val = tensor([-1, 4, 256, 2048])]; + tensor pieces_cast_fp16 = reshape(shape = var_1012, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1015_begin_0 = const()[name = string("op_1015_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1015_end_0 = const()[name = string("op_1015_end_0"), val = tensor([1, 1, 256, 2048])]; + tensor var_1015_end_mask_0 = const()[name = string("op_1015_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1015_squeeze_mask_0 = const()[name = string("op_1015_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1015_cast_fp16 = slice_by_index(begin = var_1015_begin_0, end = var_1015_end_0, end_mask = var_1015_end_mask_0, squeeze_mask = var_1015_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1018_perm_0 = const()[name = string("op_1018_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1019 = const()[name = string("op_1019"), val = tensor([-1, 524288])]; + tensor var_1018_cast_fp16 = transpose(perm = var_1018_perm_0, x = var_1015_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1019, x = var_1018_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1023_pad_0 = const()[name = string("op_1023_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1023_mode_0 = const()[name = string("op_1023_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1023_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1023_mode_0, pad = var_1023_pad_0, x = input_163_cast_fp16)[name = string("op_1023_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1023_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1026_begin_0 = const()[name = string("op_1026_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1026_end_0 = const()[name = string("op_1026_end_0"), val = tensor([1, 2, 256, 2048])]; + tensor var_1026_end_mask_0 = const()[name = string("op_1026_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1026_squeeze_mask_0 = const()[name = string("op_1026_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1026_cast_fp16 = slice_by_index(begin = var_1026_begin_0, end = var_1026_end_0, end_mask = var_1026_end_mask_0, squeeze_mask = var_1026_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1029_perm_0 = const()[name = string("op_1029_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1030 = const()[name = string("op_1030"), val = tensor([-1, 524288])]; + tensor var_1029_cast_fp16 = transpose(perm = var_1029_perm_0, x = var_1026_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1030, x = var_1029_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1034_pad_0 = const()[name = string("op_1034_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1034_mode_0 = const()[name = string("op_1034_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1034_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1034_mode_0, pad = var_1034_pad_0, x = input_165_cast_fp16)[name = string("op_1034_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1034_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1038_begin_0 = const()[name = string("op_1038_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1038_end_0 = const()[name = string("op_1038_end_0"), val = tensor([1, 3, 256, 2048])]; + tensor var_1038_end_mask_0 = const()[name = string("op_1038_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1038_squeeze_mask_0 = const()[name = string("op_1038_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1038_cast_fp16 = slice_by_index(begin = var_1038_begin_0, end = var_1038_end_0, end_mask = var_1038_end_mask_0, squeeze_mask = var_1038_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor var_1041_perm_0 = const()[name = string("op_1041_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1042 = const()[name = string("op_1042"), val = tensor([-1, 524288])]; + tensor var_1041_cast_fp16 = transpose(perm = var_1041_perm_0, x = var_1038_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1042, x = var_1041_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1046_pad_0 = const()[name = string("op_1046_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1046_mode_0 = const()[name = string("op_1046_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1046_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1046_mode_0, pad = var_1046_pad_0, x = input_167_cast_fp16)[name = string("op_1046_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1046_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1050_begin_0 = const()[name = string("op_1050_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1050_end_0 = const()[name = string("op_1050_end_0"), val = tensor([1, 4, 256, 2048])]; + tensor var_1050_end_mask_0 = const()[name = string("op_1050_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1050_squeeze_mask_0 = const()[name = string("op_1050_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1050_cast_fp16 = slice_by_index(begin = var_1050_begin_0, end = var_1050_end_0, end_mask = var_1050_end_mask_0, squeeze_mask = var_1050_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1050_cast_fp16")]; + tensor var_1053_perm_0 = const()[name = string("op_1053_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1054 = const()[name = string("op_1054"), val = tensor([-1, 524288])]; + tensor var_1053_cast_fp16 = transpose(perm = var_1053_perm_0, x = var_1050_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1054, x = var_1053_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1058_pad_0 = const()[name = string("op_1058_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1058_mode_0 = const()[name = string("op_1058_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1058_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1058_mode_0, pad = var_1058_pad_0, x = input_cast_fp16)[name = string("op_1058_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1058_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 524544])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1063_y_0_to_fp16 = const()[name = string("_inversed_1063_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29993280)))]; + tensor _inversed_1063_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1063_y_0_to_fp16)[name = string("_inversed_1063_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1063_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_128(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + int32 var_40 = const()[name = string("op_40"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_84 = maximum(x = emotion_id, y = var_38)[name = string("op_84")]; + tensor input_5 = minimum(x = var_84, y = var_40)[name = string("input_5")]; + int32 var_87_batch_dims_0 = const()[name = string("op_87_batch_dims_0"), val = int32(0)]; + bool var_87_validate_indices_0 = const()[name = string("op_87_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_87_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_87_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_87_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_87_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_87_batch_dims_0, indices = select_0_1, validate_indices = var_87_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_87_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_89_axis_0 = const()[name = string("op_89_axis_0"), val = int32(0)]; + int32 var_89_batch_dims_0 = const()[name = string("op_89_batch_dims_0"), val = int32(0)]; + bool var_89_validate_indices_0 = const()[name = string("op_89_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_89_cast_fp16_cast_uint16 = gather(axis = var_89_axis_0, batch_dims = var_89_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_89_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_89_cast_fp16_cast_uint16")]; + tensor var_90_cast_fp16 = add(x = op_87_cast_fp16_cast_uint16_cast_uint16, y = var_89_cast_fp16_cast_uint16)[name = string("op_90_cast_fp16")]; + int32 var_92_axis_0 = const()[name = string("op_92_axis_0"), val = int32(0)]; + int32 var_92_batch_dims_0 = const()[name = string("op_92_batch_dims_0"), val = int32(0)]; + bool var_92_validate_indices_0 = const()[name = string("op_92_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_92_cast_fp16_cast_uint16 = gather(axis = var_92_axis_0, batch_dims = var_92_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_92_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_92_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_90_cast_fp16, y = var_92_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_94_axes_0 = const()[name = string("op_94_axes_0"), val = tensor([-1])]; + tensor var_94_cast_fp16 = expand_dims(axes = var_94_axes_0, x = cond_cast_fp16)[name = string("op_94_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_94_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_97_axes_0 = const()[name = string("op_97_axes_0"), val = tensor([1])]; + tensor var_97 = expand_dims(axes = var_97_axes_0, x = latent_mask_1)[name = string("op_97")]; + string var_98_to_fp16_dtype_0 = const()[name = string("op_98_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_97_to_fp16 = cast(dtype = var_98_to_fp16_dtype_0, x = var_97)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_97_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_97_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_47_to_fp16 = const()[name = string("op_47_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_130_axes_0 = const()[name = string("op_130_axes_0"), val = tensor([-1])]; + tensor var_130_cast_fp16 = expand_dims(axes = var_130_axes_0, x = linear_0_cast_fp16)[name = string("op_130_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_130_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_97_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_154_axes_0 = const()[name = string("op_154_axes_0"), val = tensor([-1])]; + tensor var_154_cast_fp16 = expand_dims(axes = var_154_axes_0, x = linear_1_cast_fp16)[name = string("op_154_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_154_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_97_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_97_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_97_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_199_axes_0 = const()[name = string("op_199_axes_0"), val = tensor([-1])]; + tensor var_199_cast_fp16 = expand_dims(axes = var_199_axes_0, x = linear_2_cast_fp16)[name = string("op_199_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_199_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_97_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_223_axes_0 = const()[name = string("op_223_axes_0"), val = tensor([-1])]; + tensor var_223_cast_fp16 = expand_dims(axes = var_223_axes_0, x = linear_3_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_223_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_97_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_97_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_97_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_268_axes_0 = const()[name = string("op_268_axes_0"), val = tensor([-1])]; + tensor var_268_cast_fp16 = expand_dims(axes = var_268_axes_0, x = linear_4_cast_fp16)[name = string("op_268_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_268_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_97_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([-1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = linear_5_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_292_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_97_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_97_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_97_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_337_axes_0 = const()[name = string("op_337_axes_0"), val = tensor([-1])]; + tensor var_337_cast_fp16 = expand_dims(axes = var_337_axes_0, x = linear_6_cast_fp16)[name = string("op_337_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_337_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_97_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_361_axes_0 = const()[name = string("op_361_axes_0"), val = tensor([-1])]; + tensor var_361_cast_fp16 = expand_dims(axes = var_361_axes_0, x = linear_7_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_361_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_97_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_97_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_97_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_406_axes_0 = const()[name = string("op_406_axes_0"), val = tensor([-1])]; + tensor var_406_cast_fp16 = expand_dims(axes = var_406_axes_0, x = linear_8_cast_fp16)[name = string("op_406_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_406_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_97_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_430_axes_0 = const()[name = string("op_430_axes_0"), val = tensor([-1])]; + tensor var_430_cast_fp16 = expand_dims(axes = var_430_axes_0, x = linear_9_cast_fp16)[name = string("op_430_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_430_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_97_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_97_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_97_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_475_axes_0 = const()[name = string("op_475_axes_0"), val = tensor([-1])]; + tensor var_475_cast_fp16 = expand_dims(axes = var_475_axes_0, x = linear_10_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_475_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_97_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([-1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = linear_11_cast_fp16)[name = string("op_499_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_499_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_97_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_97_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 256])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 128, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_523_axes_0 = const()[name = string("op_523_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_523 = expand_dims(axes = var_523_axes_0, x = mask_53)[name = string("op_523")]; + string var_524_to_fp16_dtype_0 = const()[name = string("op_524_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_523_to_fp16 = cast(dtype = var_524_to_fp16_dtype_0, x = var_523)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_523_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_523_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_556_axes_0 = const()[name = string("op_556_axes_0"), val = tensor([-1])]; + tensor var_556_cast_fp16 = expand_dims(axes = var_556_axes_0, x = linear_12_cast_fp16)[name = string("op_556_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_556_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_523_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_580_axes_0 = const()[name = string("op_580_axes_0"), val = tensor([-1])]; + tensor var_580_cast_fp16 = expand_dims(axes = var_580_axes_0, x = linear_13_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_580_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_523_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_523_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_523_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_636_cast_fp16 = layer_norm(axes = var_636_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_636_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_636_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_660_cast_fp16 = layer_norm(axes = var_660_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_662_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_662_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_660_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_662_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_701_axes_0 = const()[name = string("op_701_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_701_cast_fp16 = layer_norm(axes = var_701_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_701_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_703_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_703_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_701_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_703_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_742_axes_0 = const()[name = string("op_742_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_742_cast_fp16 = layer_norm(axes = var_742_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_742_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_744_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_744_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_742_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_744_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_783_cast_fp16 = layer_norm(axes = var_783_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_783_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_785_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_785_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_783_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_785_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_824_axes_0 = const()[name = string("op_824_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_824_cast_fp16 = layer_norm(axes = var_824_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_826_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_826_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_824_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_826_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_865_axes_0 = const()[name = string("op_865_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_865_cast_fp16 = layer_norm(axes = var_865_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_867_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_867_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_865_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_867_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_906_axes_0 = const()[name = string("op_906_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_906_cast_fp16 = layer_norm(axes = var_906_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_908_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_908_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_906_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_908_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_947_axes_0 = const()[name = string("op_947_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_947_cast_fp16 = layer_norm(axes = var_947_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_947_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_949_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_949_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_947_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_949_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_976_perm_0 = const()[name = string("op_976_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_977_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_977_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_976_cast_fp16 = transpose(perm = var_976_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_977_to_fp16_quantized, x = var_976_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_983_split_sizes_0 = const()[name = string("op_983_split_sizes_0"), val = tensor([513, 513])]; + int32 var_983_axis_0 = const()[name = string("op_983_axis_0"), val = int32(1)]; + tensor var_983_cast_fp16_0, tensor var_983_cast_fp16_1 = split(axis = var_983_axis_0, split_sizes = var_983_split_sizes_0, x = values_cast_fp16)[name = string("op_983_cast_fp16")]; + tensor var_985_cast_fp16 = exp(x = var_983_cast_fp16_0)[name = string("op_985_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_985_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_987_cast_fp16 = cos(x = var_983_cast_fp16_1)[name = string("op_987_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_987_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_989_cast_fp16 = sin(x = var_983_cast_fp16_1)[name = string("op_989_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_989_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1004_pad_type_0 = const()[name = string("op_1004_pad_type_0"), val = string("valid")]; + tensor var_1004_strides_0 = const()[name = string("op_1004_strides_0"), val = tensor([1])]; + tensor var_1004_pad_0 = const()[name = string("op_1004_pad_0"), val = tensor([0, 0])]; + tensor var_1004_dilations_0 = const()[name = string("op_1004_dilations_0"), val = tensor([1])]; + int32 var_1004_groups_0 = const()[name = string("op_1004_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1004_cast_fp16 = conv(dilations = var_1004_dilations_0, groups = var_1004_groups_0, pad = var_1004_pad_0, pad_type = var_1004_pad_type_0, strides = var_1004_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1004_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1008_to_fp16 = const()[name = string("op_1008_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1008_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1011 = const()[name = string("op_1011"), val = tensor([-1, 4, 256, 256])]; + tensor pieces_cast_fp16 = reshape(shape = var_1011, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1014_begin_0 = const()[name = string("op_1014_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1014_end_0 = const()[name = string("op_1014_end_0"), val = tensor([1, 1, 256, 256])]; + tensor var_1014_end_mask_0 = const()[name = string("op_1014_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1014_squeeze_mask_0 = const()[name = string("op_1014_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1014_cast_fp16 = slice_by_index(begin = var_1014_begin_0, end = var_1014_end_0, end_mask = var_1014_end_mask_0, squeeze_mask = var_1014_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1017_perm_0 = const()[name = string("op_1017_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1018 = const()[name = string("op_1018"), val = tensor([-1, 65536])]; + tensor var_1017_cast_fp16 = transpose(perm = var_1017_perm_0, x = var_1014_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1018, x = var_1017_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1022_pad_0 = const()[name = string("op_1022_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1022_mode_0 = const()[name = string("op_1022_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1022_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1022_mode_0, pad = var_1022_pad_0, x = input_163_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1022_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1025_begin_0 = const()[name = string("op_1025_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1025_end_0 = const()[name = string("op_1025_end_0"), val = tensor([1, 2, 256, 256])]; + tensor var_1025_end_mask_0 = const()[name = string("op_1025_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1025_squeeze_mask_0 = const()[name = string("op_1025_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1025_cast_fp16 = slice_by_index(begin = var_1025_begin_0, end = var_1025_end_0, end_mask = var_1025_end_mask_0, squeeze_mask = var_1025_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1025_cast_fp16")]; + tensor var_1028_perm_0 = const()[name = string("op_1028_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1029 = const()[name = string("op_1029"), val = tensor([-1, 65536])]; + tensor var_1028_cast_fp16 = transpose(perm = var_1028_perm_0, x = var_1025_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1029, x = var_1028_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1033_pad_0 = const()[name = string("op_1033_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1033_mode_0 = const()[name = string("op_1033_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1033_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1033_mode_0, pad = var_1033_pad_0, x = input_165_cast_fp16)[name = string("op_1033_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1033_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1037_begin_0 = const()[name = string("op_1037_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1037_end_0 = const()[name = string("op_1037_end_0"), val = tensor([1, 3, 256, 256])]; + tensor var_1037_end_mask_0 = const()[name = string("op_1037_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1037_squeeze_mask_0 = const()[name = string("op_1037_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1037_cast_fp16 = slice_by_index(begin = var_1037_begin_0, end = var_1037_end_0, end_mask = var_1037_end_mask_0, squeeze_mask = var_1037_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1037_cast_fp16")]; + tensor var_1040_perm_0 = const()[name = string("op_1040_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1041 = const()[name = string("op_1041"), val = tensor([-1, 65536])]; + tensor var_1040_cast_fp16 = transpose(perm = var_1040_perm_0, x = var_1037_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1041, x = var_1040_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1045_pad_0 = const()[name = string("op_1045_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1045_mode_0 = const()[name = string("op_1045_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1045_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1045_mode_0, pad = var_1045_pad_0, x = input_167_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1045_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1049_begin_0 = const()[name = string("op_1049_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1049_end_0 = const()[name = string("op_1049_end_0"), val = tensor([1, 4, 256, 256])]; + tensor var_1049_end_mask_0 = const()[name = string("op_1049_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1049_squeeze_mask_0 = const()[name = string("op_1049_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1049_cast_fp16 = slice_by_index(begin = var_1049_begin_0, end = var_1049_end_0, end_mask = var_1049_end_mask_0, squeeze_mask = var_1049_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1049_cast_fp16")]; + tensor var_1052_perm_0 = const()[name = string("op_1052_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1053 = const()[name = string("op_1053"), val = tensor([-1, 65536])]; + tensor var_1052_cast_fp16 = transpose(perm = var_1052_perm_0, x = var_1049_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1053, x = var_1052_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1057_pad_0 = const()[name = string("op_1057_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1057_mode_0 = const()[name = string("op_1057_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1057_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1057_mode_0, pad = var_1057_pad_0, x = input_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1057_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 65792])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1062_y_0_to_fp16 = const()[name = string("_inversed_1062_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27898368)))]; + tensor _inversed_1062_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1062_y_0_to_fp16)[name = string("_inversed_1062_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1062_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_256(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + int32 var_40 = const()[name = string("op_40"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_84 = maximum(x = emotion_id, y = var_38)[name = string("op_84")]; + tensor input_5 = minimum(x = var_84, y = var_40)[name = string("input_5")]; + int32 var_87_batch_dims_0 = const()[name = string("op_87_batch_dims_0"), val = int32(0)]; + bool var_87_validate_indices_0 = const()[name = string("op_87_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_87_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_87_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_87_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_87_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_87_batch_dims_0, indices = select_0_1, validate_indices = var_87_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_87_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_89_axis_0 = const()[name = string("op_89_axis_0"), val = int32(0)]; + int32 var_89_batch_dims_0 = const()[name = string("op_89_batch_dims_0"), val = int32(0)]; + bool var_89_validate_indices_0 = const()[name = string("op_89_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_89_cast_fp16_cast_uint16 = gather(axis = var_89_axis_0, batch_dims = var_89_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_89_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_89_cast_fp16_cast_uint16")]; + tensor var_90_cast_fp16 = add(x = op_87_cast_fp16_cast_uint16_cast_uint16, y = var_89_cast_fp16_cast_uint16)[name = string("op_90_cast_fp16")]; + int32 var_92_axis_0 = const()[name = string("op_92_axis_0"), val = int32(0)]; + int32 var_92_batch_dims_0 = const()[name = string("op_92_batch_dims_0"), val = int32(0)]; + bool var_92_validate_indices_0 = const()[name = string("op_92_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_92_cast_fp16_cast_uint16 = gather(axis = var_92_axis_0, batch_dims = var_92_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_92_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_92_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_90_cast_fp16, y = var_92_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_94_axes_0 = const()[name = string("op_94_axes_0"), val = tensor([-1])]; + tensor var_94_cast_fp16 = expand_dims(axes = var_94_axes_0, x = cond_cast_fp16)[name = string("op_94_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_94_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_97_axes_0 = const()[name = string("op_97_axes_0"), val = tensor([1])]; + tensor var_97 = expand_dims(axes = var_97_axes_0, x = latent_mask_1)[name = string("op_97")]; + string var_98_to_fp16_dtype_0 = const()[name = string("op_98_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_97_to_fp16 = cast(dtype = var_98_to_fp16_dtype_0, x = var_97)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_97_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_97_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_47_to_fp16 = const()[name = string("op_47_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_130_axes_0 = const()[name = string("op_130_axes_0"), val = tensor([-1])]; + tensor var_130_cast_fp16 = expand_dims(axes = var_130_axes_0, x = linear_0_cast_fp16)[name = string("op_130_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_130_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_97_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_154_axes_0 = const()[name = string("op_154_axes_0"), val = tensor([-1])]; + tensor var_154_cast_fp16 = expand_dims(axes = var_154_axes_0, x = linear_1_cast_fp16)[name = string("op_154_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_154_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_97_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_97_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_97_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_199_axes_0 = const()[name = string("op_199_axes_0"), val = tensor([-1])]; + tensor var_199_cast_fp16 = expand_dims(axes = var_199_axes_0, x = linear_2_cast_fp16)[name = string("op_199_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_199_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_97_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_223_axes_0 = const()[name = string("op_223_axes_0"), val = tensor([-1])]; + tensor var_223_cast_fp16 = expand_dims(axes = var_223_axes_0, x = linear_3_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_223_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_97_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_97_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_97_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_268_axes_0 = const()[name = string("op_268_axes_0"), val = tensor([-1])]; + tensor var_268_cast_fp16 = expand_dims(axes = var_268_axes_0, x = linear_4_cast_fp16)[name = string("op_268_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_268_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_97_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([-1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = linear_5_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_292_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_97_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_97_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_97_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_337_axes_0 = const()[name = string("op_337_axes_0"), val = tensor([-1])]; + tensor var_337_cast_fp16 = expand_dims(axes = var_337_axes_0, x = linear_6_cast_fp16)[name = string("op_337_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_337_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_97_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_361_axes_0 = const()[name = string("op_361_axes_0"), val = tensor([-1])]; + tensor var_361_cast_fp16 = expand_dims(axes = var_361_axes_0, x = linear_7_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_361_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_97_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_97_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_97_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_406_axes_0 = const()[name = string("op_406_axes_0"), val = tensor([-1])]; + tensor var_406_cast_fp16 = expand_dims(axes = var_406_axes_0, x = linear_8_cast_fp16)[name = string("op_406_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_406_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_97_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_430_axes_0 = const()[name = string("op_430_axes_0"), val = tensor([-1])]; + tensor var_430_cast_fp16 = expand_dims(axes = var_430_axes_0, x = linear_9_cast_fp16)[name = string("op_430_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_430_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_97_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_97_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_97_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_475_axes_0 = const()[name = string("op_475_axes_0"), val = tensor([-1])]; + tensor var_475_cast_fp16 = expand_dims(axes = var_475_axes_0, x = linear_10_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_475_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_97_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([-1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = linear_11_cast_fp16)[name = string("op_499_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_499_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_97_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_97_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 512])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 256, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_523_axes_0 = const()[name = string("op_523_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_523 = expand_dims(axes = var_523_axes_0, x = mask_53)[name = string("op_523")]; + string var_524_to_fp16_dtype_0 = const()[name = string("op_524_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_523_to_fp16 = cast(dtype = var_524_to_fp16_dtype_0, x = var_523)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_523_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_523_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_556_axes_0 = const()[name = string("op_556_axes_0"), val = tensor([-1])]; + tensor var_556_cast_fp16 = expand_dims(axes = var_556_axes_0, x = linear_12_cast_fp16)[name = string("op_556_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_556_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_523_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_580_axes_0 = const()[name = string("op_580_axes_0"), val = tensor([-1])]; + tensor var_580_cast_fp16 = expand_dims(axes = var_580_axes_0, x = linear_13_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_580_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_523_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_523_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_523_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_636_cast_fp16 = layer_norm(axes = var_636_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_636_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_636_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_660_cast_fp16 = layer_norm(axes = var_660_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_662_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_662_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_660_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_662_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_701_axes_0 = const()[name = string("op_701_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_701_cast_fp16 = layer_norm(axes = var_701_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_701_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_703_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_703_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_701_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_703_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_742_axes_0 = const()[name = string("op_742_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_742_cast_fp16 = layer_norm(axes = var_742_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_742_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_744_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_744_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_742_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_744_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_783_cast_fp16 = layer_norm(axes = var_783_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_783_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_785_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_785_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_783_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_785_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_824_axes_0 = const()[name = string("op_824_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_824_cast_fp16 = layer_norm(axes = var_824_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_826_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_826_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_824_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_826_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_865_axes_0 = const()[name = string("op_865_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_865_cast_fp16 = layer_norm(axes = var_865_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_867_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_867_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_865_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_867_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_906_axes_0 = const()[name = string("op_906_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_906_cast_fp16 = layer_norm(axes = var_906_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_908_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_908_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_906_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_908_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_947_axes_0 = const()[name = string("op_947_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_947_cast_fp16 = layer_norm(axes = var_947_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_947_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_949_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_949_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_947_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_949_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_976_perm_0 = const()[name = string("op_976_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_977_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_977_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_976_cast_fp16 = transpose(perm = var_976_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_977_to_fp16_quantized, x = var_976_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_983_split_sizes_0 = const()[name = string("op_983_split_sizes_0"), val = tensor([513, 513])]; + int32 var_983_axis_0 = const()[name = string("op_983_axis_0"), val = int32(1)]; + tensor var_983_cast_fp16_0, tensor var_983_cast_fp16_1 = split(axis = var_983_axis_0, split_sizes = var_983_split_sizes_0, x = values_cast_fp16)[name = string("op_983_cast_fp16")]; + tensor var_985_cast_fp16 = exp(x = var_983_cast_fp16_0)[name = string("op_985_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_985_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_987_cast_fp16 = cos(x = var_983_cast_fp16_1)[name = string("op_987_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_987_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_989_cast_fp16 = sin(x = var_983_cast_fp16_1)[name = string("op_989_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_989_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1004_pad_type_0 = const()[name = string("op_1004_pad_type_0"), val = string("valid")]; + tensor var_1004_strides_0 = const()[name = string("op_1004_strides_0"), val = tensor([1])]; + tensor var_1004_pad_0 = const()[name = string("op_1004_pad_0"), val = tensor([0, 0])]; + tensor var_1004_dilations_0 = const()[name = string("op_1004_dilations_0"), val = tensor([1])]; + int32 var_1004_groups_0 = const()[name = string("op_1004_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1004_cast_fp16 = conv(dilations = var_1004_dilations_0, groups = var_1004_groups_0, pad = var_1004_pad_0, pad_type = var_1004_pad_type_0, strides = var_1004_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1004_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1008_to_fp16 = const()[name = string("op_1008_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1008_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1011 = const()[name = string("op_1011"), val = tensor([-1, 4, 256, 512])]; + tensor pieces_cast_fp16 = reshape(shape = var_1011, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1014_begin_0 = const()[name = string("op_1014_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1014_end_0 = const()[name = string("op_1014_end_0"), val = tensor([1, 1, 256, 512])]; + tensor var_1014_end_mask_0 = const()[name = string("op_1014_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1014_squeeze_mask_0 = const()[name = string("op_1014_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1014_cast_fp16 = slice_by_index(begin = var_1014_begin_0, end = var_1014_end_0, end_mask = var_1014_end_mask_0, squeeze_mask = var_1014_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1017_perm_0 = const()[name = string("op_1017_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1018 = const()[name = string("op_1018"), val = tensor([-1, 131072])]; + tensor var_1017_cast_fp16 = transpose(perm = var_1017_perm_0, x = var_1014_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1018, x = var_1017_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1022_pad_0 = const()[name = string("op_1022_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1022_mode_0 = const()[name = string("op_1022_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1022_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1022_mode_0, pad = var_1022_pad_0, x = input_163_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1022_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1025_begin_0 = const()[name = string("op_1025_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1025_end_0 = const()[name = string("op_1025_end_0"), val = tensor([1, 2, 256, 512])]; + tensor var_1025_end_mask_0 = const()[name = string("op_1025_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1025_squeeze_mask_0 = const()[name = string("op_1025_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1025_cast_fp16 = slice_by_index(begin = var_1025_begin_0, end = var_1025_end_0, end_mask = var_1025_end_mask_0, squeeze_mask = var_1025_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1025_cast_fp16")]; + tensor var_1028_perm_0 = const()[name = string("op_1028_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1029 = const()[name = string("op_1029"), val = tensor([-1, 131072])]; + tensor var_1028_cast_fp16 = transpose(perm = var_1028_perm_0, x = var_1025_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1029, x = var_1028_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1033_pad_0 = const()[name = string("op_1033_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1033_mode_0 = const()[name = string("op_1033_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1033_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1033_mode_0, pad = var_1033_pad_0, x = input_165_cast_fp16)[name = string("op_1033_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1033_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1037_begin_0 = const()[name = string("op_1037_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1037_end_0 = const()[name = string("op_1037_end_0"), val = tensor([1, 3, 256, 512])]; + tensor var_1037_end_mask_0 = const()[name = string("op_1037_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1037_squeeze_mask_0 = const()[name = string("op_1037_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1037_cast_fp16 = slice_by_index(begin = var_1037_begin_0, end = var_1037_end_0, end_mask = var_1037_end_mask_0, squeeze_mask = var_1037_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1037_cast_fp16")]; + tensor var_1040_perm_0 = const()[name = string("op_1040_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1041 = const()[name = string("op_1041"), val = tensor([-1, 131072])]; + tensor var_1040_cast_fp16 = transpose(perm = var_1040_perm_0, x = var_1037_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1041, x = var_1040_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1045_pad_0 = const()[name = string("op_1045_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1045_mode_0 = const()[name = string("op_1045_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1045_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1045_mode_0, pad = var_1045_pad_0, x = input_167_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1045_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1049_begin_0 = const()[name = string("op_1049_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1049_end_0 = const()[name = string("op_1049_end_0"), val = tensor([1, 4, 256, 512])]; + tensor var_1049_end_mask_0 = const()[name = string("op_1049_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1049_squeeze_mask_0 = const()[name = string("op_1049_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1049_cast_fp16 = slice_by_index(begin = var_1049_begin_0, end = var_1049_end_0, end_mask = var_1049_end_mask_0, squeeze_mask = var_1049_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1049_cast_fp16")]; + tensor var_1052_perm_0 = const()[name = string("op_1052_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1053 = const()[name = string("op_1053"), val = tensor([-1, 131072])]; + tensor var_1052_cast_fp16 = transpose(perm = var_1052_perm_0, x = var_1049_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1053, x = var_1052_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1057_pad_0 = const()[name = string("op_1057_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1057_mode_0 = const()[name = string("op_1057_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1057_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1057_mode_0, pad = var_1057_pad_0, x = input_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1057_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 131328])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1062_y_0_to_fp16 = const()[name = string("_inversed_1062_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28028992)))]; + tensor _inversed_1062_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1062_y_0_to_fp16)[name = string("_inversed_1062_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1062_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_384(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + int32 var_40 = const()[name = string("op_40"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_84 = maximum(x = emotion_id, y = var_38)[name = string("op_84")]; + tensor input_5 = minimum(x = var_84, y = var_40)[name = string("input_5")]; + int32 var_87_batch_dims_0 = const()[name = string("op_87_batch_dims_0"), val = int32(0)]; + bool var_87_validate_indices_0 = const()[name = string("op_87_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_87_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_87_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_87_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_87_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_87_batch_dims_0, indices = select_0_1, validate_indices = var_87_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_87_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_89_axis_0 = const()[name = string("op_89_axis_0"), val = int32(0)]; + int32 var_89_batch_dims_0 = const()[name = string("op_89_batch_dims_0"), val = int32(0)]; + bool var_89_validate_indices_0 = const()[name = string("op_89_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_89_cast_fp16_cast_uint16 = gather(axis = var_89_axis_0, batch_dims = var_89_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_89_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_89_cast_fp16_cast_uint16")]; + tensor var_90_cast_fp16 = add(x = op_87_cast_fp16_cast_uint16_cast_uint16, y = var_89_cast_fp16_cast_uint16)[name = string("op_90_cast_fp16")]; + int32 var_92_axis_0 = const()[name = string("op_92_axis_0"), val = int32(0)]; + int32 var_92_batch_dims_0 = const()[name = string("op_92_batch_dims_0"), val = int32(0)]; + bool var_92_validate_indices_0 = const()[name = string("op_92_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_92_cast_fp16_cast_uint16 = gather(axis = var_92_axis_0, batch_dims = var_92_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_92_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_92_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_90_cast_fp16, y = var_92_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_94_axes_0 = const()[name = string("op_94_axes_0"), val = tensor([-1])]; + tensor var_94_cast_fp16 = expand_dims(axes = var_94_axes_0, x = cond_cast_fp16)[name = string("op_94_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_94_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_97_axes_0 = const()[name = string("op_97_axes_0"), val = tensor([1])]; + tensor var_97 = expand_dims(axes = var_97_axes_0, x = latent_mask_1)[name = string("op_97")]; + string var_98_to_fp16_dtype_0 = const()[name = string("op_98_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_97_to_fp16 = cast(dtype = var_98_to_fp16_dtype_0, x = var_97)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_97_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_97_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_47_to_fp16 = const()[name = string("op_47_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_130_axes_0 = const()[name = string("op_130_axes_0"), val = tensor([-1])]; + tensor var_130_cast_fp16 = expand_dims(axes = var_130_axes_0, x = linear_0_cast_fp16)[name = string("op_130_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_130_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_97_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_154_axes_0 = const()[name = string("op_154_axes_0"), val = tensor([-1])]; + tensor var_154_cast_fp16 = expand_dims(axes = var_154_axes_0, x = linear_1_cast_fp16)[name = string("op_154_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_154_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_97_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_97_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_97_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_199_axes_0 = const()[name = string("op_199_axes_0"), val = tensor([-1])]; + tensor var_199_cast_fp16 = expand_dims(axes = var_199_axes_0, x = linear_2_cast_fp16)[name = string("op_199_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_199_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_97_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_223_axes_0 = const()[name = string("op_223_axes_0"), val = tensor([-1])]; + tensor var_223_cast_fp16 = expand_dims(axes = var_223_axes_0, x = linear_3_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_223_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_97_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_97_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_97_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_268_axes_0 = const()[name = string("op_268_axes_0"), val = tensor([-1])]; + tensor var_268_cast_fp16 = expand_dims(axes = var_268_axes_0, x = linear_4_cast_fp16)[name = string("op_268_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_268_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_97_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([-1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = linear_5_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_292_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_97_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_97_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_97_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_337_axes_0 = const()[name = string("op_337_axes_0"), val = tensor([-1])]; + tensor var_337_cast_fp16 = expand_dims(axes = var_337_axes_0, x = linear_6_cast_fp16)[name = string("op_337_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_337_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_97_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_361_axes_0 = const()[name = string("op_361_axes_0"), val = tensor([-1])]; + tensor var_361_cast_fp16 = expand_dims(axes = var_361_axes_0, x = linear_7_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_361_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_97_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_97_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_97_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_406_axes_0 = const()[name = string("op_406_axes_0"), val = tensor([-1])]; + tensor var_406_cast_fp16 = expand_dims(axes = var_406_axes_0, x = linear_8_cast_fp16)[name = string("op_406_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_406_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_97_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_430_axes_0 = const()[name = string("op_430_axes_0"), val = tensor([-1])]; + tensor var_430_cast_fp16 = expand_dims(axes = var_430_axes_0, x = linear_9_cast_fp16)[name = string("op_430_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_430_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_97_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_97_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_97_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_475_axes_0 = const()[name = string("op_475_axes_0"), val = tensor([-1])]; + tensor var_475_cast_fp16 = expand_dims(axes = var_475_axes_0, x = linear_10_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_475_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_97_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([-1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = linear_11_cast_fp16)[name = string("op_499_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_499_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_97_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_97_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 768])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 384, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_523_axes_0 = const()[name = string("op_523_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_523 = expand_dims(axes = var_523_axes_0, x = mask_53)[name = string("op_523")]; + string var_524_to_fp16_dtype_0 = const()[name = string("op_524_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_523_to_fp16 = cast(dtype = var_524_to_fp16_dtype_0, x = var_523)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_523_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_523_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_556_axes_0 = const()[name = string("op_556_axes_0"), val = tensor([-1])]; + tensor var_556_cast_fp16 = expand_dims(axes = var_556_axes_0, x = linear_12_cast_fp16)[name = string("op_556_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_556_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_523_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_580_axes_0 = const()[name = string("op_580_axes_0"), val = tensor([-1])]; + tensor var_580_cast_fp16 = expand_dims(axes = var_580_axes_0, x = linear_13_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_580_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_523_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_523_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_523_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_636_cast_fp16 = layer_norm(axes = var_636_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_636_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_636_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_660_cast_fp16 = layer_norm(axes = var_660_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_662_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_662_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_660_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_662_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_701_axes_0 = const()[name = string("op_701_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_701_cast_fp16 = layer_norm(axes = var_701_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_701_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_703_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_703_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_701_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_703_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_742_axes_0 = const()[name = string("op_742_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_742_cast_fp16 = layer_norm(axes = var_742_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_742_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_744_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_744_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_742_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_744_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_783_cast_fp16 = layer_norm(axes = var_783_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_783_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_785_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_785_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_783_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_785_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_824_axes_0 = const()[name = string("op_824_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_824_cast_fp16 = layer_norm(axes = var_824_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_826_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_826_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_824_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_826_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_865_axes_0 = const()[name = string("op_865_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_865_cast_fp16 = layer_norm(axes = var_865_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_867_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_867_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_865_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_867_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_906_axes_0 = const()[name = string("op_906_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_906_cast_fp16 = layer_norm(axes = var_906_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_908_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_908_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_906_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_908_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_947_axes_0 = const()[name = string("op_947_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_947_cast_fp16 = layer_norm(axes = var_947_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_947_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_949_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_949_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_947_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_949_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_976_perm_0 = const()[name = string("op_976_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_977_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_977_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_976_cast_fp16 = transpose(perm = var_976_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_977_to_fp16_quantized, x = var_976_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_983_split_sizes_0 = const()[name = string("op_983_split_sizes_0"), val = tensor([513, 513])]; + int32 var_983_axis_0 = const()[name = string("op_983_axis_0"), val = int32(1)]; + tensor var_983_cast_fp16_0, tensor var_983_cast_fp16_1 = split(axis = var_983_axis_0, split_sizes = var_983_split_sizes_0, x = values_cast_fp16)[name = string("op_983_cast_fp16")]; + tensor var_985_cast_fp16 = exp(x = var_983_cast_fp16_0)[name = string("op_985_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_985_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_987_cast_fp16 = cos(x = var_983_cast_fp16_1)[name = string("op_987_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_987_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_989_cast_fp16 = sin(x = var_983_cast_fp16_1)[name = string("op_989_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_989_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1004_pad_type_0 = const()[name = string("op_1004_pad_type_0"), val = string("valid")]; + tensor var_1004_strides_0 = const()[name = string("op_1004_strides_0"), val = tensor([1])]; + tensor var_1004_pad_0 = const()[name = string("op_1004_pad_0"), val = tensor([0, 0])]; + tensor var_1004_dilations_0 = const()[name = string("op_1004_dilations_0"), val = tensor([1])]; + int32 var_1004_groups_0 = const()[name = string("op_1004_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1004_cast_fp16 = conv(dilations = var_1004_dilations_0, groups = var_1004_groups_0, pad = var_1004_pad_0, pad_type = var_1004_pad_type_0, strides = var_1004_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1004_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1008_to_fp16 = const()[name = string("op_1008_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1008_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1011 = const()[name = string("op_1011"), val = tensor([-1, 4, 256, 768])]; + tensor pieces_cast_fp16 = reshape(shape = var_1011, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1014_begin_0 = const()[name = string("op_1014_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1014_end_0 = const()[name = string("op_1014_end_0"), val = tensor([1, 1, 256, 768])]; + tensor var_1014_end_mask_0 = const()[name = string("op_1014_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1014_squeeze_mask_0 = const()[name = string("op_1014_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1014_cast_fp16 = slice_by_index(begin = var_1014_begin_0, end = var_1014_end_0, end_mask = var_1014_end_mask_0, squeeze_mask = var_1014_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1017_perm_0 = const()[name = string("op_1017_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1018 = const()[name = string("op_1018"), val = tensor([-1, 196608])]; + tensor var_1017_cast_fp16 = transpose(perm = var_1017_perm_0, x = var_1014_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1018, x = var_1017_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1022_pad_0 = const()[name = string("op_1022_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1022_mode_0 = const()[name = string("op_1022_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1022_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1022_mode_0, pad = var_1022_pad_0, x = input_163_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1022_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1025_begin_0 = const()[name = string("op_1025_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1025_end_0 = const()[name = string("op_1025_end_0"), val = tensor([1, 2, 256, 768])]; + tensor var_1025_end_mask_0 = const()[name = string("op_1025_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1025_squeeze_mask_0 = const()[name = string("op_1025_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1025_cast_fp16 = slice_by_index(begin = var_1025_begin_0, end = var_1025_end_0, end_mask = var_1025_end_mask_0, squeeze_mask = var_1025_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1025_cast_fp16")]; + tensor var_1028_perm_0 = const()[name = string("op_1028_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1029 = const()[name = string("op_1029"), val = tensor([-1, 196608])]; + tensor var_1028_cast_fp16 = transpose(perm = var_1028_perm_0, x = var_1025_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1029, x = var_1028_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1033_pad_0 = const()[name = string("op_1033_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1033_mode_0 = const()[name = string("op_1033_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1033_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1033_mode_0, pad = var_1033_pad_0, x = input_165_cast_fp16)[name = string("op_1033_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1033_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1037_begin_0 = const()[name = string("op_1037_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1037_end_0 = const()[name = string("op_1037_end_0"), val = tensor([1, 3, 256, 768])]; + tensor var_1037_end_mask_0 = const()[name = string("op_1037_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1037_squeeze_mask_0 = const()[name = string("op_1037_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1037_cast_fp16 = slice_by_index(begin = var_1037_begin_0, end = var_1037_end_0, end_mask = var_1037_end_mask_0, squeeze_mask = var_1037_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1037_cast_fp16")]; + tensor var_1040_perm_0 = const()[name = string("op_1040_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1041 = const()[name = string("op_1041"), val = tensor([-1, 196608])]; + tensor var_1040_cast_fp16 = transpose(perm = var_1040_perm_0, x = var_1037_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1041, x = var_1040_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1045_pad_0 = const()[name = string("op_1045_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1045_mode_0 = const()[name = string("op_1045_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1045_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1045_mode_0, pad = var_1045_pad_0, x = input_167_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1045_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1049_begin_0 = const()[name = string("op_1049_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1049_end_0 = const()[name = string("op_1049_end_0"), val = tensor([1, 4, 256, 768])]; + tensor var_1049_end_mask_0 = const()[name = string("op_1049_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1049_squeeze_mask_0 = const()[name = string("op_1049_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1049_cast_fp16 = slice_by_index(begin = var_1049_begin_0, end = var_1049_end_0, end_mask = var_1049_end_mask_0, squeeze_mask = var_1049_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1049_cast_fp16")]; + tensor var_1052_perm_0 = const()[name = string("op_1052_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1053 = const()[name = string("op_1053"), val = tensor([-1, 196608])]; + tensor var_1052_cast_fp16 = transpose(perm = var_1052_perm_0, x = var_1049_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1053, x = var_1052_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1057_pad_0 = const()[name = string("op_1057_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1057_mode_0 = const()[name = string("op_1057_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1057_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1057_mode_0, pad = var_1057_pad_0, x = input_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1057_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 196864])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1062_y_0_to_fp16 = const()[name = string("_inversed_1062_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28290688)))]; + tensor _inversed_1062_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1062_y_0_to_fp16)[name = string("_inversed_1062_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1062_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_512(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 var_41 = const()[name = string("op_41"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_85 = maximum(x = emotion_id, y = var_39)[name = string("op_85")]; + tensor input_5 = minimum(x = var_85, y = var_41)[name = string("input_5")]; + int32 var_88_batch_dims_0 = const()[name = string("op_88_batch_dims_0"), val = int32(0)]; + bool var_88_validate_indices_0 = const()[name = string("op_88_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_88_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_88_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_88_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_88_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_88_batch_dims_0, indices = select_0_1, validate_indices = var_88_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_88_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_90_axis_0 = const()[name = string("op_90_axis_0"), val = int32(0)]; + int32 var_90_batch_dims_0 = const()[name = string("op_90_batch_dims_0"), val = int32(0)]; + bool var_90_validate_indices_0 = const()[name = string("op_90_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_90_cast_fp16_cast_uint16 = gather(axis = var_90_axis_0, batch_dims = var_90_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_90_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_90_cast_fp16_cast_uint16")]; + tensor var_91_cast_fp16 = add(x = op_88_cast_fp16_cast_uint16_cast_uint16, y = var_90_cast_fp16_cast_uint16)[name = string("op_91_cast_fp16")]; + int32 var_93_axis_0 = const()[name = string("op_93_axis_0"), val = int32(0)]; + int32 var_93_batch_dims_0 = const()[name = string("op_93_batch_dims_0"), val = int32(0)]; + bool var_93_validate_indices_0 = const()[name = string("op_93_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_93_cast_fp16_cast_uint16 = gather(axis = var_93_axis_0, batch_dims = var_93_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_93_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_93_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_91_cast_fp16, y = var_93_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_95_axes_0 = const()[name = string("op_95_axes_0"), val = tensor([-1])]; + tensor var_95_cast_fp16 = expand_dims(axes = var_95_axes_0, x = cond_cast_fp16)[name = string("op_95_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_95_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_98_axes_0 = const()[name = string("op_98_axes_0"), val = tensor([1])]; + tensor var_98 = expand_dims(axes = var_98_axes_0, x = latent_mask_1)[name = string("op_98")]; + string var_99_to_fp16_dtype_0 = const()[name = string("op_99_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_98_to_fp16 = cast(dtype = var_99_to_fp16_dtype_0, x = var_98)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_98_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_98_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_131_axes_0 = const()[name = string("op_131_axes_0"), val = tensor([-1])]; + tensor var_131_cast_fp16 = expand_dims(axes = var_131_axes_0, x = linear_0_cast_fp16)[name = string("op_131_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_131_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_98_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_axes_0 = const()[name = string("op_155_axes_0"), val = tensor([-1])]; + tensor var_155_cast_fp16 = expand_dims(axes = var_155_axes_0, x = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_155_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_98_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_98_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_98_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_200_axes_0 = const()[name = string("op_200_axes_0"), val = tensor([-1])]; + tensor var_200_cast_fp16 = expand_dims(axes = var_200_axes_0, x = linear_2_cast_fp16)[name = string("op_200_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_200_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_98_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_224_axes_0 = const()[name = string("op_224_axes_0"), val = tensor([-1])]; + tensor var_224_cast_fp16 = expand_dims(axes = var_224_axes_0, x = linear_3_cast_fp16)[name = string("op_224_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_224_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_98_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_98_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_98_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_269_axes_0 = const()[name = string("op_269_axes_0"), val = tensor([-1])]; + tensor var_269_cast_fp16 = expand_dims(axes = var_269_axes_0, x = linear_4_cast_fp16)[name = string("op_269_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_269_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_98_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_293_axes_0 = const()[name = string("op_293_axes_0"), val = tensor([-1])]; + tensor var_293_cast_fp16 = expand_dims(axes = var_293_axes_0, x = linear_5_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_293_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_98_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_98_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_98_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([-1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = linear_6_cast_fp16)[name = string("op_338_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_338_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_98_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([-1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = linear_7_cast_fp16)[name = string("op_362_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_362_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_98_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_98_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_98_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_407_axes_0 = const()[name = string("op_407_axes_0"), val = tensor([-1])]; + tensor var_407_cast_fp16 = expand_dims(axes = var_407_axes_0, x = linear_8_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_407_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_98_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_431_axes_0 = const()[name = string("op_431_axes_0"), val = tensor([-1])]; + tensor var_431_cast_fp16 = expand_dims(axes = var_431_axes_0, x = linear_9_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_431_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_98_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_98_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_98_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + tensor var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = linear_10_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_476_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_98_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_500_axes_0 = const()[name = string("op_500_axes_0"), val = tensor([-1])]; + tensor var_500_cast_fp16 = expand_dims(axes = var_500_axes_0, x = linear_11_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_500_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_98_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_98_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 1024])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 512, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_524 = expand_dims(axes = var_524_axes_0, x = mask_53)[name = string("op_524")]; + string var_525_to_fp16_dtype_0 = const()[name = string("op_525_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_524_to_fp16 = cast(dtype = var_525_to_fp16_dtype_0, x = var_524)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_524_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_524_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_557_axes_0 = const()[name = string("op_557_axes_0"), val = tensor([-1])]; + tensor var_557_cast_fp16 = expand_dims(axes = var_557_axes_0, x = linear_12_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_557_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_524_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_581_axes_0 = const()[name = string("op_581_axes_0"), val = tensor([-1])]; + tensor var_581_cast_fp16 = expand_dims(axes = var_581_axes_0, x = linear_13_cast_fp16)[name = string("op_581_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_581_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_524_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_524_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_524_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_637_axes_0 = const()[name = string("op_637_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_36_to_fp16 = const()[name = string("op_36_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_637_cast_fp16 = layer_norm(axes = var_637_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_637_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_637_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_663_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_663_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_661_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_663_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_702_axes_0 = const()[name = string("op_702_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_702_cast_fp16 = layer_norm(axes = var_702_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_702_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_704_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_704_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_702_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_704_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_743_axes_0 = const()[name = string("op_743_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_743_cast_fp16 = layer_norm(axes = var_743_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_743_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_745_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_745_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_743_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_745_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_784_axes_0 = const()[name = string("op_784_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_784_cast_fp16 = layer_norm(axes = var_784_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_784_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_786_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_786_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_784_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_786_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_825_cast_fp16 = layer_norm(axes = var_825_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_825_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_827_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_827_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_825_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_827_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_866_cast_fp16 = layer_norm(axes = var_866_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_868_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_868_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_866_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_868_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_907_axes_0 = const()[name = string("op_907_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_907_cast_fp16 = layer_norm(axes = var_907_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_907_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_909_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_909_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_907_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_909_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_948_axes_0 = const()[name = string("op_948_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_948_cast_fp16 = layer_norm(axes = var_948_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_948_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_950_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_950_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_948_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_950_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_978_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_978_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_978_to_fp16_quantized, x = var_977_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_984_split_sizes_0 = const()[name = string("op_984_split_sizes_0"), val = tensor([513, 513])]; + int32 var_984_axis_0 = const()[name = string("op_984_axis_0"), val = int32(1)]; + tensor var_984_cast_fp16_0, tensor var_984_cast_fp16_1 = split(axis = var_984_axis_0, split_sizes = var_984_split_sizes_0, x = values_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_986_cast_fp16 = exp(x = var_984_cast_fp16_0)[name = string("op_986_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_986_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_988_cast_fp16 = cos(x = var_984_cast_fp16_1)[name = string("op_988_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_988_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_990_cast_fp16 = sin(x = var_984_cast_fp16_1)[name = string("op_990_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_990_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1005_pad_type_0 = const()[name = string("op_1005_pad_type_0"), val = string("valid")]; + tensor var_1005_strides_0 = const()[name = string("op_1005_strides_0"), val = tensor([1])]; + tensor var_1005_pad_0 = const()[name = string("op_1005_pad_0"), val = tensor([0, 0])]; + tensor var_1005_dilations_0 = const()[name = string("op_1005_dilations_0"), val = tensor([1])]; + int32 var_1005_groups_0 = const()[name = string("op_1005_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1005_cast_fp16 = conv(dilations = var_1005_dilations_0, groups = var_1005_groups_0, pad = var_1005_pad_0, pad_type = var_1005_pad_type_0, strides = var_1005_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1005_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1005_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1009_to_fp16 = const()[name = string("op_1009_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1009_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1012 = const()[name = string("op_1012"), val = tensor([-1, 4, 256, 1024])]; + tensor pieces_cast_fp16 = reshape(shape = var_1012, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1015_begin_0 = const()[name = string("op_1015_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1015_end_0 = const()[name = string("op_1015_end_0"), val = tensor([1, 1, 256, 1024])]; + tensor var_1015_end_mask_0 = const()[name = string("op_1015_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1015_squeeze_mask_0 = const()[name = string("op_1015_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1015_cast_fp16 = slice_by_index(begin = var_1015_begin_0, end = var_1015_end_0, end_mask = var_1015_end_mask_0, squeeze_mask = var_1015_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1018_perm_0 = const()[name = string("op_1018_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1019 = const()[name = string("op_1019"), val = tensor([-1, 262144])]; + tensor var_1018_cast_fp16 = transpose(perm = var_1018_perm_0, x = var_1015_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1019, x = var_1018_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1023_pad_0 = const()[name = string("op_1023_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1023_mode_0 = const()[name = string("op_1023_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1023_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1023_mode_0, pad = var_1023_pad_0, x = input_163_cast_fp16)[name = string("op_1023_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1023_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1026_begin_0 = const()[name = string("op_1026_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1026_end_0 = const()[name = string("op_1026_end_0"), val = tensor([1, 2, 256, 1024])]; + tensor var_1026_end_mask_0 = const()[name = string("op_1026_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1026_squeeze_mask_0 = const()[name = string("op_1026_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1026_cast_fp16 = slice_by_index(begin = var_1026_begin_0, end = var_1026_end_0, end_mask = var_1026_end_mask_0, squeeze_mask = var_1026_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1029_perm_0 = const()[name = string("op_1029_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1030 = const()[name = string("op_1030"), val = tensor([-1, 262144])]; + tensor var_1029_cast_fp16 = transpose(perm = var_1029_perm_0, x = var_1026_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1030, x = var_1029_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1034_pad_0 = const()[name = string("op_1034_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1034_mode_0 = const()[name = string("op_1034_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1034_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1034_mode_0, pad = var_1034_pad_0, x = input_165_cast_fp16)[name = string("op_1034_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1034_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1038_begin_0 = const()[name = string("op_1038_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1038_end_0 = const()[name = string("op_1038_end_0"), val = tensor([1, 3, 256, 1024])]; + tensor var_1038_end_mask_0 = const()[name = string("op_1038_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1038_squeeze_mask_0 = const()[name = string("op_1038_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1038_cast_fp16 = slice_by_index(begin = var_1038_begin_0, end = var_1038_end_0, end_mask = var_1038_end_mask_0, squeeze_mask = var_1038_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor var_1041_perm_0 = const()[name = string("op_1041_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1042 = const()[name = string("op_1042"), val = tensor([-1, 262144])]; + tensor var_1041_cast_fp16 = transpose(perm = var_1041_perm_0, x = var_1038_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1042, x = var_1041_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1046_pad_0 = const()[name = string("op_1046_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1046_mode_0 = const()[name = string("op_1046_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1046_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1046_mode_0, pad = var_1046_pad_0, x = input_167_cast_fp16)[name = string("op_1046_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1046_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1050_begin_0 = const()[name = string("op_1050_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1050_end_0 = const()[name = string("op_1050_end_0"), val = tensor([1, 4, 256, 1024])]; + tensor var_1050_end_mask_0 = const()[name = string("op_1050_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1050_squeeze_mask_0 = const()[name = string("op_1050_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1050_cast_fp16 = slice_by_index(begin = var_1050_begin_0, end = var_1050_end_0, end_mask = var_1050_end_mask_0, squeeze_mask = var_1050_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1050_cast_fp16")]; + tensor var_1053_perm_0 = const()[name = string("op_1053_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1054 = const()[name = string("op_1054"), val = tensor([-1, 262144])]; + tensor var_1053_cast_fp16 = transpose(perm = var_1053_perm_0, x = var_1050_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1054, x = var_1053_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1058_pad_0 = const()[name = string("op_1058_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1058_mode_0 = const()[name = string("op_1058_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1058_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1058_mode_0, pad = var_1058_pad_0, x = input_cast_fp16)[name = string("op_1058_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1058_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 262400])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1063_y_0_to_fp16 = const()[name = string("_inversed_1063_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28683456)))]; + tensor _inversed_1063_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1063_y_0_to_fp16)[name = string("_inversed_1063_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1063_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_768(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 var_41 = const()[name = string("op_41"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_85 = maximum(x = emotion_id, y = var_39)[name = string("op_85")]; + tensor input_5 = minimum(x = var_85, y = var_41)[name = string("input_5")]; + int32 var_88_batch_dims_0 = const()[name = string("op_88_batch_dims_0"), val = int32(0)]; + bool var_88_validate_indices_0 = const()[name = string("op_88_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_88_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_88_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_88_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_88_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_88_batch_dims_0, indices = select_0_1, validate_indices = var_88_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_88_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_90_axis_0 = const()[name = string("op_90_axis_0"), val = int32(0)]; + int32 var_90_batch_dims_0 = const()[name = string("op_90_batch_dims_0"), val = int32(0)]; + bool var_90_validate_indices_0 = const()[name = string("op_90_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_90_cast_fp16_cast_uint16 = gather(axis = var_90_axis_0, batch_dims = var_90_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_90_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_90_cast_fp16_cast_uint16")]; + tensor var_91_cast_fp16 = add(x = op_88_cast_fp16_cast_uint16_cast_uint16, y = var_90_cast_fp16_cast_uint16)[name = string("op_91_cast_fp16")]; + int32 var_93_axis_0 = const()[name = string("op_93_axis_0"), val = int32(0)]; + int32 var_93_batch_dims_0 = const()[name = string("op_93_batch_dims_0"), val = int32(0)]; + bool var_93_validate_indices_0 = const()[name = string("op_93_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_93_cast_fp16_cast_uint16 = gather(axis = var_93_axis_0, batch_dims = var_93_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_93_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_93_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_91_cast_fp16, y = var_93_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_95_axes_0 = const()[name = string("op_95_axes_0"), val = tensor([-1])]; + tensor var_95_cast_fp16 = expand_dims(axes = var_95_axes_0, x = cond_cast_fp16)[name = string("op_95_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_95_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_98_axes_0 = const()[name = string("op_98_axes_0"), val = tensor([1])]; + tensor var_98 = expand_dims(axes = var_98_axes_0, x = latent_mask_1)[name = string("op_98")]; + string var_99_to_fp16_dtype_0 = const()[name = string("op_99_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_98_to_fp16 = cast(dtype = var_99_to_fp16_dtype_0, x = var_98)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_98_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_98_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_131_axes_0 = const()[name = string("op_131_axes_0"), val = tensor([-1])]; + tensor var_131_cast_fp16 = expand_dims(axes = var_131_axes_0, x = linear_0_cast_fp16)[name = string("op_131_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_131_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_98_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_axes_0 = const()[name = string("op_155_axes_0"), val = tensor([-1])]; + tensor var_155_cast_fp16 = expand_dims(axes = var_155_axes_0, x = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_155_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_98_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_98_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_98_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_200_axes_0 = const()[name = string("op_200_axes_0"), val = tensor([-1])]; + tensor var_200_cast_fp16 = expand_dims(axes = var_200_axes_0, x = linear_2_cast_fp16)[name = string("op_200_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_200_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_98_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_224_axes_0 = const()[name = string("op_224_axes_0"), val = tensor([-1])]; + tensor var_224_cast_fp16 = expand_dims(axes = var_224_axes_0, x = linear_3_cast_fp16)[name = string("op_224_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_224_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_98_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_98_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_98_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_269_axes_0 = const()[name = string("op_269_axes_0"), val = tensor([-1])]; + tensor var_269_cast_fp16 = expand_dims(axes = var_269_axes_0, x = linear_4_cast_fp16)[name = string("op_269_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_269_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_98_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_293_axes_0 = const()[name = string("op_293_axes_0"), val = tensor([-1])]; + tensor var_293_cast_fp16 = expand_dims(axes = var_293_axes_0, x = linear_5_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_293_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_98_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_98_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_98_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([-1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = linear_6_cast_fp16)[name = string("op_338_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_338_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_98_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([-1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = linear_7_cast_fp16)[name = string("op_362_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_362_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_98_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_98_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_98_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_407_axes_0 = const()[name = string("op_407_axes_0"), val = tensor([-1])]; + tensor var_407_cast_fp16 = expand_dims(axes = var_407_axes_0, x = linear_8_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_407_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_98_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_431_axes_0 = const()[name = string("op_431_axes_0"), val = tensor([-1])]; + tensor var_431_cast_fp16 = expand_dims(axes = var_431_axes_0, x = linear_9_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_431_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_98_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_98_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_98_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + tensor var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = linear_10_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_476_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_98_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_500_axes_0 = const()[name = string("op_500_axes_0"), val = tensor([-1])]; + tensor var_500_cast_fp16 = expand_dims(axes = var_500_axes_0, x = linear_11_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_500_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_98_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_98_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 1536])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 768, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_524 = expand_dims(axes = var_524_axes_0, x = mask_53)[name = string("op_524")]; + string var_525_to_fp16_dtype_0 = const()[name = string("op_525_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_524_to_fp16 = cast(dtype = var_525_to_fp16_dtype_0, x = var_524)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_524_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_524_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_557_axes_0 = const()[name = string("op_557_axes_0"), val = tensor([-1])]; + tensor var_557_cast_fp16 = expand_dims(axes = var_557_axes_0, x = linear_12_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_557_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_524_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_581_axes_0 = const()[name = string("op_581_axes_0"), val = tensor([-1])]; + tensor var_581_cast_fp16 = expand_dims(axes = var_581_axes_0, x = linear_13_cast_fp16)[name = string("op_581_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_581_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_524_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_524_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_524_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_637_axes_0 = const()[name = string("op_637_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_36_to_fp16 = const()[name = string("op_36_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_637_cast_fp16 = layer_norm(axes = var_637_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_637_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_637_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_663_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_663_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_661_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_663_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_702_axes_0 = const()[name = string("op_702_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_702_cast_fp16 = layer_norm(axes = var_702_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_702_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_704_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_704_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_702_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_704_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_743_axes_0 = const()[name = string("op_743_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_743_cast_fp16 = layer_norm(axes = var_743_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_743_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_745_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_745_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_743_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_745_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_784_axes_0 = const()[name = string("op_784_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_784_cast_fp16 = layer_norm(axes = var_784_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_784_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_786_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_786_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_784_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_786_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_825_cast_fp16 = layer_norm(axes = var_825_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_825_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_827_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_827_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_825_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_827_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_866_cast_fp16 = layer_norm(axes = var_866_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_868_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_868_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_866_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_868_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_907_axes_0 = const()[name = string("op_907_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_907_cast_fp16 = layer_norm(axes = var_907_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_907_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_909_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_909_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_907_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_909_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_948_axes_0 = const()[name = string("op_948_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_948_cast_fp16 = layer_norm(axes = var_948_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_948_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_950_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_950_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_948_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_950_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_978_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_978_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_978_to_fp16_quantized, x = var_977_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_984_split_sizes_0 = const()[name = string("op_984_split_sizes_0"), val = tensor([513, 513])]; + int32 var_984_axis_0 = const()[name = string("op_984_axis_0"), val = int32(1)]; + tensor var_984_cast_fp16_0, tensor var_984_cast_fp16_1 = split(axis = var_984_axis_0, split_sizes = var_984_split_sizes_0, x = values_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_986_cast_fp16 = exp(x = var_984_cast_fp16_0)[name = string("op_986_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_986_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_988_cast_fp16 = cos(x = var_984_cast_fp16_1)[name = string("op_988_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_988_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_990_cast_fp16 = sin(x = var_984_cast_fp16_1)[name = string("op_990_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_990_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1005_pad_type_0 = const()[name = string("op_1005_pad_type_0"), val = string("valid")]; + tensor var_1005_strides_0 = const()[name = string("op_1005_strides_0"), val = tensor([1])]; + tensor var_1005_pad_0 = const()[name = string("op_1005_pad_0"), val = tensor([0, 0])]; + tensor var_1005_dilations_0 = const()[name = string("op_1005_dilations_0"), val = tensor([1])]; + int32 var_1005_groups_0 = const()[name = string("op_1005_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1005_cast_fp16 = conv(dilations = var_1005_dilations_0, groups = var_1005_groups_0, pad = var_1005_pad_0, pad_type = var_1005_pad_type_0, strides = var_1005_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1005_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1005_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1009_to_fp16 = const()[name = string("op_1009_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1009_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1012 = const()[name = string("op_1012"), val = tensor([-1, 4, 256, 1536])]; + tensor pieces_cast_fp16 = reshape(shape = var_1012, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1015_begin_0 = const()[name = string("op_1015_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1015_end_0 = const()[name = string("op_1015_end_0"), val = tensor([1, 1, 256, 1536])]; + tensor var_1015_end_mask_0 = const()[name = string("op_1015_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1015_squeeze_mask_0 = const()[name = string("op_1015_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1015_cast_fp16 = slice_by_index(begin = var_1015_begin_0, end = var_1015_end_0, end_mask = var_1015_end_mask_0, squeeze_mask = var_1015_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1018_perm_0 = const()[name = string("op_1018_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1019 = const()[name = string("op_1019"), val = tensor([-1, 393216])]; + tensor var_1018_cast_fp16 = transpose(perm = var_1018_perm_0, x = var_1015_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1019, x = var_1018_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1023_pad_0 = const()[name = string("op_1023_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1023_mode_0 = const()[name = string("op_1023_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1023_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1023_mode_0, pad = var_1023_pad_0, x = input_163_cast_fp16)[name = string("op_1023_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1023_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1026_begin_0 = const()[name = string("op_1026_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1026_end_0 = const()[name = string("op_1026_end_0"), val = tensor([1, 2, 256, 1536])]; + tensor var_1026_end_mask_0 = const()[name = string("op_1026_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1026_squeeze_mask_0 = const()[name = string("op_1026_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1026_cast_fp16 = slice_by_index(begin = var_1026_begin_0, end = var_1026_end_0, end_mask = var_1026_end_mask_0, squeeze_mask = var_1026_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1029_perm_0 = const()[name = string("op_1029_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1030 = const()[name = string("op_1030"), val = tensor([-1, 393216])]; + tensor var_1029_cast_fp16 = transpose(perm = var_1029_perm_0, x = var_1026_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1030, x = var_1029_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1034_pad_0 = const()[name = string("op_1034_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1034_mode_0 = const()[name = string("op_1034_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1034_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1034_mode_0, pad = var_1034_pad_0, x = input_165_cast_fp16)[name = string("op_1034_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1034_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1038_begin_0 = const()[name = string("op_1038_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1038_end_0 = const()[name = string("op_1038_end_0"), val = tensor([1, 3, 256, 1536])]; + tensor var_1038_end_mask_0 = const()[name = string("op_1038_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1038_squeeze_mask_0 = const()[name = string("op_1038_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1038_cast_fp16 = slice_by_index(begin = var_1038_begin_0, end = var_1038_end_0, end_mask = var_1038_end_mask_0, squeeze_mask = var_1038_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor var_1041_perm_0 = const()[name = string("op_1041_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1042 = const()[name = string("op_1042"), val = tensor([-1, 393216])]; + tensor var_1041_cast_fp16 = transpose(perm = var_1041_perm_0, x = var_1038_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1042, x = var_1041_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1046_pad_0 = const()[name = string("op_1046_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1046_mode_0 = const()[name = string("op_1046_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1046_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1046_mode_0, pad = var_1046_pad_0, x = input_167_cast_fp16)[name = string("op_1046_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1046_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1050_begin_0 = const()[name = string("op_1050_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1050_end_0 = const()[name = string("op_1050_end_0"), val = tensor([1, 4, 256, 1536])]; + tensor var_1050_end_mask_0 = const()[name = string("op_1050_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1050_squeeze_mask_0 = const()[name = string("op_1050_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1050_cast_fp16 = slice_by_index(begin = var_1050_begin_0, end = var_1050_end_0, end_mask = var_1050_end_mask_0, squeeze_mask = var_1050_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1050_cast_fp16")]; + tensor var_1053_perm_0 = const()[name = string("op_1053_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1054 = const()[name = string("op_1054"), val = tensor([-1, 393216])]; + tensor var_1053_cast_fp16 = transpose(perm = var_1053_perm_0, x = var_1050_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1054, x = var_1053_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1058_pad_0 = const()[name = string("op_1058_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1058_mode_0 = const()[name = string("op_1058_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1058_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1058_mode_0, pad = var_1058_pad_0, x = input_cast_fp16)[name = string("op_1058_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1058_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 393472])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1063_y_0_to_fp16 = const()[name = string("_inversed_1063_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29207296)))]; + tensor _inversed_1063_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1063_y_0_to_fp16)[name = string("_inversed_1063_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1063_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); +} \ No newline at end of file diff --git a/coreml/coreml/vocoder.mlmodelc/weights/weight.bin b/coreml/coreml/vocoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..d06a87eaaeebb1533daf67b38cbeb9f07fe75a30 --- /dev/null +++ b/coreml/coreml/vocoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f787f1a85ba0b25deb1baa8144e3bb8cf4095a5c16690e2e0700cc53402dcf5 +size 31041408 diff --git a/coreml/manifest.json b/coreml/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..01f10feac43639890e523caf03758c264484a328 --- /dev/null +++ b/coreml/manifest.json @@ -0,0 +1,1477 @@ +{ + "architecture": "scyllasband-duration-flow", + "assets": { + "fixed_delivery_references": "assets/fixed_references.json", + "g2p_config": "assets/g2p/export_config.json", + "g2p_language_map": "assets/g2p/language_map.json", + "g2p_normalization": "assets/g2p/normalization.json", + "g2p_phoneme_dict": "assets/g2p/phoneme_dict.json", + "g2p_pronunciation_overrides": "assets/g2p/pronunciation_overrides.json", + "g2p_tokenizer": "assets/g2p/tokenizer.json", + "language_index": "assets/languages.json", + "phone_vocab": "assets/phone_vocab.json", + "voice_index": "assets/voices.json" + }, + "audio": { + "hop_length": 256, + "latent_dim": 24, + "latent_hop_length": 512, + "n_mels": 100, + "sample_rate": 24000 + }, + "components": { + "g2p_64": { + "artifacts": { + "coreml": { + "format": "coreml", + "path": "coreml/g2p.mlmodelc", + "function": "g2p_64" + } + }, + "inputs": [ + "text" + ], + "outputs": [ + "logits" + ], + "required": false + }, + "g2p_128": { + "artifacts": { + "coreml": { + "format": "coreml", + "path": "coreml/g2p.mlmodelc", + "function": "g2p_128" + } + }, + "inputs": [ + "text" + ], + "outputs": [ + "logits" + ], + "required": false + }, + 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