diff --git a/README.md b/README.md index 98730b20b5a7e5ec1b5c79763f1ad28f8cd9f25b..1f671155d81efb138e1ec423bec26b5ce7320912 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ tags: [text-to-speech, tts, litert, onnx, coreml, coreai, pytorch, multilingual, # Scylla's Band v2 -Release **`v2-20261006`**. The [Scylla's Band runtime](https://github.com/lowkeytea/scyllasband) supports this release. Tag `v2-20261006-apple` adds Core ML and Core AI bundles of the same model; the runtime downloads from that tag. +Release **`v2-20261020`**. The [Scylla's Band runtime](https://github.com/lowkeytea/scyllasband) supports this release. Tag `v2-20261020-apple` adds Core ML and Core AI bundles of the same model; the runtime downloads from that tag. The model exposes **energy, tension, valence and assertiveness** on **0–4 scales with neutral at 2**. Combining the controls changes the generated delivery; a request is not a guarantee of a particular perceived emotion or exact external scorer value. @@ -55,7 +55,7 @@ Combining the controls gives familiar deliveries: | Angry | `energy=2.3,tension=2.4,valence=1.4,assertiveness=2.4` | | Sad | `energy=1.7,tension=1.5,valence=1.4,assertiveness=2` | -These are moderate styles; how strongly each comes through varies with voice, language and text. The [voice gallery](https://lowkeytea.github.io/scyllasband/#delivery) plays each of them for all ten voices. +These are moderate styles; how strongly each comes through varies with voice, language and text. The [voice gallery](https://lowkeytea.github.io/scyllasband/#delivery) plays each of them for all fourteen voices. `--delivery auto` omits all coordinates. A per-axis `auto` omits just that coordinate; other unspecified fields in a partial request use neutral defaults. Omission is represented by a separate mask, not by the value 2. It is not an automatic text-to-performance planner. @@ -72,7 +72,7 @@ Controls are **global within each request**. This release does not support contr | Audio | Mono 24 kHz; 100 mel features; 24 latent channels | | Mel / latent hop | 256 / 512 samples | | Duration model | Hidden size 320; 8 layers; reads the neighbouring text as context | -| Vector model | Hidden size 512; 12 layers; adaptive layer normalization and QK normalization | +| Vector model | Hidden size 640; 16 layers; adaptive layer normalization and QK normalization | | Maximum graph phones per sentence | 512 | | Trained target length | 64–420 latent frames (about 1.4–9 s) per pass | | Latent buckets | 128, 256, 384, 512, 768, 1024 frames | @@ -114,16 +114,17 @@ The GPU is the default on Apple platforms. The Neural Engine placement runs the ## Voices and language coverage -Ten voices: Ariadne, Felix, Gwen, Ink, Max, Orpheus, Rex, Scylla, Stone, Tuesday. +Fourteen voices: Ariadne, Felix, Gwen, Ink, Kincade, Max, Orpheus, Poppy, Priya, Rex, Scylla, Stone, Thistle, Tuesday. -All ten have Spanish (`es`), Italian (`it`), French (`fr`), German (`de`) and Vietnamese (`vi`). Ink, Orpheus and Tuesday use British English (`en_gb`); the other seven use American English (`en_us`). This is **60 trained voice/locale pairs**, not every combination of ten voices and seven locale IDs. +All fourteen have Spanish (`es`), Italian (`it`), French (`fr`), German (`de`) and Vietnamese (`vi`). Ink, Orpheus and Tuesday use British English (`en_gb`); the other eleven use American English (`en_us`). This is **84 trained voice/locale pairs**, not every combination of fourteen voices and seven locale IDs. ## Artifacts -- `litert/` (164 MB): LiteRT bundle; default download. Transformer and vocoder weights are dynamic-range INT8; each size bucket is a signature of one model file, sharing weights. +- `litert/` (228 MB): LiteRT bundle; default download. Transformer and vocoder weights are dynamic-range INT8; each size bucket is a signature of one model file, sharing weights. - `coreai/` (155 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; size buckets are functions of one asset sharing weights. The flow also runs 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/` (142 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. -- `onnx/` (262 MB): ONNX Runtime bundle with dynamic INT8 transformer weights; the vocoder stays in full precision. +- `onnx/` (323 MB): ONNX Runtime bundle with dynamic INT8 transformer weights; the vocoder stays in full precision. +- `webgpu/` (325 MB): ONNX Runtime bundle for ONNX Runtime Web's WebGPU backend, used by the browser demo. Transformer weights are INT8 with float compute (`MatMulNBits`), which runs entirely on the GPU; the vocoder stays in full precision. - `pytorch/`: duration, vector, adapter/vocoder and G2P weights (no training state), with the release configuration assets. - `SHA256SUMS.json`: published-file hashes. @@ -134,8 +135,8 @@ Every runtime bundle includes the G2P model in a matching precision. The Apple b Release validation separates graph accuracy, frontend/timing behaviour and perceived speech quality: - Component comparisons against PyTorch during export, and end-to-end comparison of full passages against the PyTorch reference path. -- The distilled flow at 4 Euler steps compared with the base flow at 8 Heun steps on the same passages, seeds and noise: equal distance to the 32-step output, confirmed by listening across all ten voices. -- Quantized bundles compared with full precision on real sentences: identical phonemes, phone durations and generated latents within small tolerances, followed by listening across all ten voices. +- The distilled flow at 4 Euler steps compared with the base flow on the same passages, seeds and noise: closer to the base flow's 32-step output than the base flow's own 8 Heun steps, and compared blind with the 32-step base flow by listening across all fourteen voices. +- Quantized bundles compared with full precision on real sentences (48 passages, six voices): identical phonemes; identical phone durations for 97.7% (ONNX), 97.9% (LiteRT) and 99.6% (WebGPU) of phones, the rest one frame apart; generated latents within 2% median relative error; followed by listening across all fourteen voices. - G2P conversions compared with the full-precision G2P on multilingual text. - Core ML and Core AI bundles compared with the PyTorch model on every graph and on real passages, on each compute unit they are used with (GPU, CPU, and for Core AI the Neural Engine): identical phonemes apart from two near-tie stress marks also seen in FP16, fewer changed phone durations than the LiteRT bundle, and smaller 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 flow's residual stream; in FP32 the rewritten graphs are bit-identical to the trained ones. diff --git a/SHA256SUMS.json b/SHA256SUMS.json index c153ebb1b1dca77f33f200fb944fce9deb602043..4b08d9222db03a0e2bca1f0ea34396c490dd8ec6 100644 --- a/SHA256SUMS.json +++ b/SHA256SUMS.json @@ -4,244 +4,16 @@ "bytes": 11357 }, "README.md": { - "sha256": "059fae18d14420f7cc376c606059157175c948015a9633ea0c057b6154f765dd", - "bytes": 12553 + "sha256": "a510621d1dd98a0c4d0aa0ee0665c82d7ffce200bea3c566d130d3548ef7f3d4", + "bytes": 13095 }, "THIRD_PARTY_NOTICES.md": { - "sha256": "3b793b8ee250cc711e4012ee42e9625a8a7e788bf12cab85a1abbd17b17644ca", - "bytes": 2818 - }, - "coreai/assets/fixed_references.json": { - "sha256": 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a/coreml/assets/voices.json b/coreml/assets/voices.json deleted file mode 100644 index b5ee729b27aa83bdd20342937b1ad82408cecadd..0000000000000000000000000000000000000000 --- a/coreml/assets/voices.json +++ /dev/null @@ -1,42 +0,0 @@ -[ - { - "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 - } -] diff --git a/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin b/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 547fc4db7a44bcc16c3a76dee2a59538270434e8..0000000000000000000000000000000000000000 --- a/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ 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{"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(35968))))[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(70)]; - 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(70)]; - 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(36224)))]; - 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(40768)))]; - 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(45312)))]; - 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(49856)))]; - 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(50112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133696))))[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(134336))), scale = tensor([[[0x1.28cp-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(298240)))]; - 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(304704)))]; - 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 = const()[name = string("module_model_voice_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305408)))]; - 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)[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(311872)))]; - 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(316416)))]; - 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(317120)))]; - 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(317248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327552))))[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(328256)))]; - 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(328960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339264))))[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(339968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(503872))))[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(504576)))]; - 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(505664)))]; - 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(506368)))]; - 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(507072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(814336))))[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(816320)))]; - 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(818304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(920768))))[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(921472)))]; - 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(922176)))]; - 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(922880)))]; - 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(923584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333248))))[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(1335872)))]; - 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(1338496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1748160))))[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(1748864)))]; - 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(1749568)))]; - 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(1750272)))]; - 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(1750976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2058240))))[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(2060224)))]; - 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(2062208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2164672))))[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(2165376)))]; - 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(2166080)))]; - 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(2166784)))]; - 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(2167488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2577152))))[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(2579776)))]; - 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(2582400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2992064))))[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(2992768)))]; - 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(2993472)))]; - 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(2994176)))]; - 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(2994880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3302144))))[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(3304128)))]; - 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(3306112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3408576))))[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(3409280)))]; - 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(3409984)))]; - 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(3410688)))]; - 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(3411392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3821056))))[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(3823680)))]; - 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(3826304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4235968))))[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(4236672)))]; - 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(4237376)))]; - 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(4238080)))]; - 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(4238784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4546048))))[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(4548032)))]; - 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(4550016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4652480))))[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(4653184)))]; - 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(4653888)))]; - 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(4654592)))]; - 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(4655296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5064960))))[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(5067584)))]; - 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(5070208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5479872))))[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(5480576)))]; - 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(5481280)))]; - 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(5481984)))]; - 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(5482688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5789952))))[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(5791936)))]; - 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(5793920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5896384))))[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(5897088)))]; - 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(5897792)))]; - 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(5898496)))]; - 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(5899200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6308864))))[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(6311488)))]; - 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(6314112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6723776))))[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(6724480)))]; - 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(6725184)))]; - 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(6725888)))]; - 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(6726592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7033856))))[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(7035840)))]; - 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(7037824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7140288))))[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(7140992)))]; - 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(7141696)))]; - 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(7142400)))]; - 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(7143104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7552768))))[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(7555392)))]; - 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(7558016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7967680))))[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(7968384)))]; - 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(7969088)))]; - 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(7969792)))]; - 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(7970496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8277760))))[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(8279744)))]; - 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(8281728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8384192))))[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(8384896)))]; - 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(8385600)))]; - 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(8386304)))]; - 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(8387008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8796672))))[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(8799296)))]; - 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(8801920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9211584))))[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(9212288)))]; - 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(9212992)))]; - 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(9213696)))]; - 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(9214400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9521664))))[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(9523648)))]; - 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(9525632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9628096))))[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(9628800)))]; - 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(9629504)))]; - 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(9630208)))]; - 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(9630912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10040576))))[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(10043200)))]; - 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(10045824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10455488))))[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(10456192)))]; - 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(10456896)))]; - 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(10457600)))]; - 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(10458304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10560768))))[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(10561472)))]; - 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(10562176)))]; - tensor module_model_head_3_bias_to_fp16 = const()[name = string("module_model_head_3_bias_to_fp16"), val = tensor([0x1.6b4p-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 deleted file mode 100644 index 8f29d949951f0c8558f09f2e720b466bce8e7eab..0000000000000000000000000000000000000000 --- a/coreml/coreml/duration_predictor.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5383e19195d6807cf6632d2c82b51b86aaa947ba51d8042434f73b5039c70ea4 -size 10562880 diff --git a/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin b/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 2b7922e08de2e3f77a34622800cdac1047f67f7a..0000000000000000000000000000000000000000 --- a/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:70e1596126ef45b7692226041b4f56570d9b381d9f0ad5714c8dee3c745a762e -size 243 diff --git a/coreml/coreml/g2p.mlmodelc/coremldata.bin b/coreml/coreml/g2p.mlmodelc/coremldata.bin deleted file mode 100644 index 2ff1a68088f4fbf7352225e1ef0264f583f1a13f..0000000000000000000000000000000000000000 --- a/coreml/coreml/g2p.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -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 deleted file mode 100644 index 8ca2158e00759b1324097247d1902774774b26a2..0000000000000000000000000000000000000000 --- a/coreml/coreml/g2p.mlmodelc/model.mil +++ /dev/null @@ -1,2100 +0,0 @@ -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 deleted file mode 100644 index dd5c633871d0edfb7a0f555b71498a0a2b9ac49b..0000000000000000000000000000000000000000 --- a/coreml/coreml/g2p.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:045da2991ed2a42ba027aaa6b83813054f410b6eedfdce82f2ab287ec4ca74e2 -size 27346752 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index a04f9e7d2764962af02bd6ebc3f724f532bc6e59..0000000000000000000000000000000000000000 --- a/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d23387f42991cc2675cfa0ed4d8830615bb3f8e760916cce4ba7d3e54c48de21 -size 243 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin b/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin deleted file mode 100644 index 89107293f14097119173c53fc853bda3e0c84bb3..0000000000000000000000000000000000000000 --- a/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:118d56856570862aa0e3dde8d3cacdc600d4147da424ec88a22fac56c20cf493 -size 476 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/model.mil b/coreml/coreml/vector_context_encoder.mlmodelc/model.mil deleted file mode 100644 index ad2811e8901c10ff15b8f7c7ff21f24fde90f076..0000000000000000000000000000000000000000 --- a/coreml/coreml/vector_context_encoder.mlmodelc/model.mil +++ /dev/null @@ -1,267 +0,0 @@ -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 deleted file mode 100644 index 0c0d686528bfc3a56f94594844f95d2efd1dc620..0000000000000000000000000000000000000000 --- a/coreml/coreml/vector_context_encoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:70da5deb1236e2f5d3250cef65ee0619f1089ebb7c5fc39744d205d45bf24c66 -size 7572352 diff --git a/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index 3bac6151df9072edec7ebd3e776d308ae893a1b4..0000000000000000000000000000000000000000 --- a/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:13eab20d76b95b33201ab96dd522599eabd282b36db8c231832834862d4385b7 -size 243 diff --git a/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin b/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin deleted file mode 100644 index d4ba13213be427c3f7e2b6f5c325c87ecaf785a5..0000000000000000000000000000000000000000 --- a/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d22410a5a41fc0aae79224588c2da505cd4abae60526d06fd989749001741762 -size 3647 diff --git a/coreml/coreml/vector_estimator.mlmodelc/model.mil b/coreml/coreml/vector_estimator.mlmodelc/model.mil deleted file mode 100644 index 85050ebbbe540e3cd90273ed1ff21b2d63899590..0000000000000000000000000000000000000000 --- a/coreml/coreml/vector_estimator.mlmodelc/model.mil +++ /dev/null @@ -1,8086 +0,0 @@ -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_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_33 = const()[name = string("op_33"), val = int32(0)]; - tensor present = not_equal(x = delivery_present, y = var_33)[name = string("present")]; - int32 var_35 = const()[name = string("op_35"), val = int32(0)]; - tensor latent_mask_1 = not_equal(x = latent_mask, y = var_35)[name = string("latent_mask")]; - int32 var_37 = const()[name = string("op_37"), val = int32(0)]; - tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_37)[name = string("prefix_mask_1")]; - int32 var_58 = const()[name = string("op_58"), val = int32(-1)]; - int32 var_73 = const()[name = string("op_73"), val = int32(1)]; - int32 var_87 = const()[name = string("op_87"), val = int32(7)]; - tensor var_88 = mul(x = voice_id, y = var_87)[name = string("op_88")]; - tensor index = add(x = var_88, 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(35968))))[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_20")]; - tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_19")]; - 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(70)]; - 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_18")]; - tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_17")]; - 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(70)]; - 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(36224)))]; - 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_16")]; - 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(40768)))]; - 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(45312)))]; - 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(49856)))]; - 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(50112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62464))))[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(63552)))]; - tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_15")]; - 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_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_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109248))))[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_14")]; - tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; - tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; - int32 var_150_axis_0 = const()[name = string("op_150_axis_0"), val = int32(0)]; - int32 var_150_batch_dims_0 = const()[name = string("op_150_batch_dims_0"), val = int32(0)]; - bool var_150_validate_indices_0 = const()[name = string("op_150_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(109504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154624))))[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_13")]; - tensor var_150_cast_fp16_cast_uint16 = gather(axis = var_150_axis_0, batch_dims = var_150_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_150_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_150_cast_fp16_cast_uint16")]; - int32 var_155_axis_0 = const()[name = string("op_155_axis_0"), val = int32(0)]; - int32 var_155_batch_dims_0 = const()[name = string("op_155_batch_dims_0"), val = int32(0)]; - bool var_155_validate_indices_0 = const()[name = string("op_155_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(154880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187712))))[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_12")]; - tensor var_155_cast_fp16_cast_uint16 = gather(axis = var_155_axis_0, batch_dims = var_155_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_155_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_155_cast_fp16_cast_uint16")]; - tensor hidden_7_cast_fp16 = add(x = var_150_cast_fp16_cast_uint16, y = var_155_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_10")]; - 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_11")]; - 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(187904)))]; - 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(190016)))]; - 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_166_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_166_cast_fp16")]; - tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_166_cast_fp16)[name = string("hidden_11_cast_fp16")]; - int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(0)]; - int32 var_170_batch_dims_0 = const()[name = string("op_170_batch_dims_0"), val = int32(0)]; - bool var_170_validate_indices_0 = const()[name = string("op_170_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(191104)))]; - 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_9")]; - tensor var_170_cast_fp16_cast_uint16 = gather(axis = var_170_axis_0, batch_dims = var_170_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_170_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_170_cast_fp16_cast_uint16")]; - tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_170_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; - tensor op_174_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60227392))), scale = tensor([[[0x1.46cp-5]]]))[name = string("op_174_to_fp16_quantized")]; - tensor hidden_17_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_174_to_fp16_quantized)[name = string("hidden_17_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(261888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267072))))[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(267200)))]; - 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_186_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_186_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_58, interleave = input_15_interleave_0, values = (var_186_cast_fp16, present_to_fp16))[name = string("input_15_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(274432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279616))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; - 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(280704)))]; - tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; - tensor var_204_axes_0 = const()[name = string("op_204_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_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = time_to_fp16)[name = string("op_204_cast_fp16")]; - tensor var_205_to_fp16 = const()[name = string("op_205_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281792)))]; - tensor phases_1_cast_fp16 = mul(x = var_204_cast_fp16, y = var_205_to_fp16)[name = string("phases_1_cast_fp16")]; - fp16 var_207_to_fp16 = const()[name = string("op_207_to_fp16"), val = fp16(0x1.92p+2)]; - tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_207_to_fp16)[name = string("phases_cast_fp16")]; - tensor var_209_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_209_cast_fp16")]; - tensor var_210_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_210_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_58, interleave = input_21_interleave_0, values = (var_209_cast_fp16, var_210_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(282368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(544576))))[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(545664)))]; - 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(546752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808960))))[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(810048)))]; - 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_220_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_220_cast_fp16")]; - tensor var_221_cast_fp16 = add(x = var_220_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_221_cast_fp16")]; - tensor var_222_cast_fp16 = add(x = var_221_cast_fp16, y = linear_2_cast_fp16)[name = string("op_222_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(811136)))]; - tensor cond_1_cast_fp16 = add(x = var_222_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; - fp16 var_61_to_fp16 = const()[name = string("op_61_to_fp16"), val = fp16(0x0p+0)]; - fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x1p+0)]; - tensor clip_0_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; - tensor clip_1_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; - tensor var_236_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_236_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(812224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(828672))))[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(829760)))]; - 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_236_cast_fp16)[name = string("linear_5_cast_fp16")]; - tensor var_242_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_242_cast_fp16")]; - tensor var_243_axes_0 = const()[name = string("op_243_axes_0"), val = tensor([-1])]; - tensor var_243_cast_fp16 = expand_dims(axes = var_243_axes_0, x = clip_1_cast_fp16)[name = string("op_243_cast_fp16")]; - tensor var_244_cast_fp16 = mul(x = var_242_cast_fp16, y = var_243_cast_fp16)[name = string("op_244_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(830848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(847296))))[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_244_cast_fp16)[name = string("linear_6_cast_fp16")]; - tensor var_250_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_250_cast_fp16")]; - tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_250_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_260_axes_0 = const()[name = string("op_260_axes_0"), val = tensor([-1])]; - tensor var_260_cast_fp16 = expand_dims(axes = var_260_axes_0, x = mask_7_cast_fp16)[name = string("op_260_cast_fp16")]; - tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_260_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(848384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110592))))[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(1111680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1373888))))[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_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_37_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_37_cast_fp16")]; - tensor input_33_perm_0 = const()[name = string("input_33_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_33_cast_fp16 = transpose(perm = input_33_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; - tensor linear_9_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_9_cast_fp16")]; - tensor op_277_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1374976))), scale = tensor([[[0x1.278p-5]]]))[name = string("op_277_to_fp16_quantized")]; - tensor context_3_cast_fp16 = add(x = linear_9_cast_fp16, y = op_277_to_fp16_quantized)[name = string("context_3_cast_fp16")]; - tensor var_280_to_fp16 = const()[name = string("op_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1424192)))]; - tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_280_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_73, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_17_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_73, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; - fp16 var_288_to_fp16 = const()[name = string("op_288_to_fp16"), val = fp16(0x1p-3)]; - tensor input_41_cast_fp16 = mul(x = x_1_cast_fp16, y = var_288_to_fp16)[name = string("input_41_cast_fp16")]; - tensor input_39_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("input_39_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(1425280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2998208))))[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(3004416)))]; - tensor linear_10_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_39_cast_fp16)[name = string("linear_10_cast_fp16")]; - tensor var_322_split_sizes_0 = const()[name = string("op_322_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_322_axis_0 = const()[name = string("op_322_axis_0"), val = int32(-1)]; - tensor var_322_cast_fp16_0, tensor var_322_cast_fp16_1, tensor var_322_cast_fp16_2, tensor var_322_cast_fp16_3, tensor var_322_cast_fp16_4, tensor var_322_cast_fp16_5 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = linear_10_cast_fp16)[name = string("op_322_cast_fp16")]; - tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; - tensor var_328_cast_fp16 = expand_dims(axes = var_328_axes_0, x = var_322_cast_fp16_2)[name = string("op_328_cast_fp16")]; - tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; - fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x1.8p-23)]; - tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_69_to_fp16, x = input_41_cast_fp16)[name = string("x_3_cast_fp16")]; - tensor var_331_axes_0 = const()[name = string("op_331_axes_0"), val = tensor([1])]; - tensor var_331_cast_fp16 = expand_dims(axes = var_331_axes_0, x = var_322_cast_fp16_1)[name = string("op_331_cast_fp16")]; - fp16 var_332_to_fp16 = const()[name = string("op_332_to_fp16"), val = fp16(0x1p+0)]; - tensor var_333_cast_fp16 = add(x = var_331_cast_fp16, y = var_332_to_fp16)[name = string("op_333_cast_fp16")]; - tensor var_334_cast_fp16 = mul(x = x_3_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_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_322_cast_fp16_0)[name = string("op_335_cast_fp16")]; - tensor x_5_cast_fp16 = add(x = var_334_cast_fp16, y = var_335_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(3010624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3797120))))[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(3800256)))]; - tensor linear_11_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_11_cast_fp16")]; - tensor var_347 = const()[name = string("op_347"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_1_cast_fp16 = reshape(shape = var_347, x = linear_11_cast_fp16)[name = string("qkv_1_cast_fp16")]; - tensor var_349_split_sizes_0 = const()[name = string("op_349_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_349_axis_0 = const()[name = string("op_349_axis_0"), val = int32(2)]; - tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1, tensor var_349_cast_fp16_2 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_349_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_349_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_349_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_349_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; - tensor var_354_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_354_cast_fp16")]; - tensor var_356_axes_0 = const()[name = string("op_356_axes_0"), val = tensor([-1])]; - bool var_356_keep_dims_0 = const()[name = string("op_356_keep_dims_0"), val = bool(true)]; - tensor var_356_cast_fp16 = reduce_mean(axes = var_356_axes_0, keep_dims = var_356_keep_dims_0, x = var_354_cast_fp16)[name = string("op_356_cast_fp16")]; - fp16 var_357_to_fp16 = const()[name = string("op_357_to_fp16"), val = fp16(0x1p-23)]; - tensor var_358_cast_fp16 = add(x = var_356_cast_fp16, y = var_357_to_fp16)[name = string("op_358_cast_fp16")]; - fp32 var_359_epsilon_0 = const()[name = string("op_359_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_359_cast_fp16 = rsqrt(epsilon = var_359_epsilon_0, x = var_358_cast_fp16)[name = string("op_359_cast_fp16")]; - tensor var_360_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_359_cast_fp16)[name = string("op_360_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(3803392)))]; - tensor var_361_cast_fp16 = mul(x = var_360_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_361_cast_fp16")]; - tensor var_364_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_364_cast_fp16")]; - tensor var_366_axes_0 = const()[name = string("op_366_axes_0"), val = tensor([-1])]; - bool var_366_keep_dims_0 = const()[name = string("op_366_keep_dims_0"), val = bool(true)]; - tensor var_366_cast_fp16 = reduce_mean(axes = var_366_axes_0, keep_dims = var_366_keep_dims_0, x = var_364_cast_fp16)[name = string("op_366_cast_fp16")]; - fp16 var_367_to_fp16 = const()[name = string("op_367_to_fp16"), val = fp16(0x1p-23)]; - tensor var_368_cast_fp16 = add(x = var_366_cast_fp16, y = var_367_to_fp16)[name = string("op_368_cast_fp16")]; - fp32 var_369_epsilon_0 = const()[name = string("op_369_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_369_cast_fp16 = rsqrt(epsilon = var_369_epsilon_0, x = var_368_cast_fp16)[name = string("op_369_cast_fp16")]; - tensor var_370_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_369_cast_fp16)[name = string("op_370_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(3803584)))]; - tensor var_371_cast_fp16 = mul(x = var_370_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_371_cast_fp16")]; - tensor var_376 = const()[name = string("op_376"), val = tensor([1, 1, 1, 1120])]; - tensor var_377 = reshape(shape = var_376, x = valid_mask)[name = string("op_377")]; - tensor var_379 = logical_not(x = var_377)[name = string("op_379")]; - fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(-inf)]; - tensor var_375_to_fp16 = const()[name = string("op_375_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60751744)))]; - tensor attn_mask_1_cast_fp16 = select(a = var_68_to_fp16, b = var_375_to_fp16, cond = var_379)[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_371_cast_fp16)[name = string("transpose_155")]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_361_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_382_perm_0 = const()[name = string("op_382_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1120, 512])]; - tensor var_382_cast_fp16 = transpose(perm = var_382_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; - tensor input_43_cast_fp16 = reshape(shape = var_383, x = var_382_cast_fp16)[name = string("input_43_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(3804288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4066496))))[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(4067584)))]; - tensor linear_12_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_43_cast_fp16)[name = string("linear_12_cast_fp16")]; - tensor var_389_cast_fp16 = mul(x = var_328_cast_fp16, y = linear_12_cast_fp16)[name = string("op_389_cast_fp16")]; - tensor input_47_cast_fp16 = add(x = input_41_cast_fp16, y = var_389_cast_fp16)[name = string("input_47_cast_fp16")]; - tensor var_391_axes_0 = const()[name = string("op_391_axes_0"), val = tensor([1])]; - tensor var_391_cast_fp16 = expand_dims(axes = var_391_axes_0, x = var_322_cast_fp16_5)[name = string("op_391_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_69_to_fp16, x = input_47_cast_fp16)[name = string("x_7_cast_fp16")]; - tensor var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor([1])]; - tensor var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_322_cast_fp16_4)[name = string("op_394_cast_fp16")]; - fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p+0)]; - tensor var_396_cast_fp16 = add(x = var_394_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; - tensor var_397_cast_fp16 = mul(x = x_7_cast_fp16, y = var_396_cast_fp16)[name = string("op_397_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_322_cast_fp16_3)[name = string("op_398_cast_fp16")]; - tensor input_49_cast_fp16 = add(x = var_397_cast_fp16, y = var_398_cast_fp16)[name = string("input_49_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(4068672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5117312))))[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(5121472)))]; - tensor linear_13_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_49_cast_fp16)[name = string("linear_13_cast_fp16")]; - string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; - tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_13_cast_fp16)[name = string("input_53_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(5125632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6174272))))[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(6175360)))]; - tensor linear_14_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_53_cast_fp16)[name = string("linear_14_cast_fp16")]; - tensor var_411_cast_fp16 = mul(x = var_391_cast_fp16, y = linear_14_cast_fp16)[name = string("op_411_cast_fp16")]; - tensor input_61_cast_fp16 = add(x = input_47_cast_fp16, y = var_411_cast_fp16)[name = string("input_61_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(6176448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7749376))))[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(7755584)))]; - tensor linear_15_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_39_cast_fp16)[name = string("linear_15_cast_fp16")]; - tensor var_420_split_sizes_0 = const()[name = string("op_420_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_420_axis_0 = const()[name = string("op_420_axis_0"), val = int32(-1)]; - tensor var_420_cast_fp16_0, tensor var_420_cast_fp16_1, tensor var_420_cast_fp16_2, tensor var_420_cast_fp16_3, tensor var_420_cast_fp16_4, tensor var_420_cast_fp16_5 = split(axis = var_420_axis_0, split_sizes = var_420_split_sizes_0, x = linear_15_cast_fp16)[name = string("op_420_cast_fp16")]; - tensor var_426_axes_0 = const()[name = string("op_426_axes_0"), val = tensor([1])]; - tensor var_426_cast_fp16 = expand_dims(axes = var_426_axes_0, x = var_420_cast_fp16_2)[name = string("op_426_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_69_to_fp16, x = input_61_cast_fp16)[name = string("x_9_cast_fp16")]; - tensor var_429_axes_0 = const()[name = string("op_429_axes_0"), val = tensor([1])]; - tensor var_429_cast_fp16 = expand_dims(axes = var_429_axes_0, x = var_420_cast_fp16_1)[name = string("op_429_cast_fp16")]; - fp16 var_430_to_fp16 = const()[name = string("op_430_to_fp16"), val = fp16(0x1p+0)]; - tensor var_431_cast_fp16 = add(x = var_429_cast_fp16, y = var_430_to_fp16)[name = string("op_431_cast_fp16")]; - tensor var_432_cast_fp16 = mul(x = x_9_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_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_420_cast_fp16_0)[name = string("op_433_cast_fp16")]; - tensor x_11_cast_fp16 = add(x = var_432_cast_fp16, y = var_433_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(7761792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548288))))[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(8551424)))]; - tensor linear_16_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_16_cast_fp16")]; - tensor var_445 = const()[name = string("op_445"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_3_cast_fp16 = reshape(shape = var_445, x = linear_16_cast_fp16)[name = string("qkv_3_cast_fp16")]; - tensor var_447_split_sizes_0 = const()[name = string("op_447_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_447_axis_0 = const()[name = string("op_447_axis_0"), val = int32(2)]; - tensor var_447_cast_fp16_0, tensor var_447_cast_fp16_1, tensor var_447_cast_fp16_2 = split(axis = var_447_axis_0, split_sizes = var_447_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_447_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_447_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_447_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_447_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; - tensor var_452_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_452_cast_fp16")]; - tensor var_454_axes_0 = const()[name = string("op_454_axes_0"), val = tensor([-1])]; - bool var_454_keep_dims_0 = const()[name = string("op_454_keep_dims_0"), val = bool(true)]; - tensor var_454_cast_fp16 = reduce_mean(axes = var_454_axes_0, keep_dims = var_454_keep_dims_0, x = var_452_cast_fp16)[name = string("op_454_cast_fp16")]; - fp16 var_455_to_fp16 = const()[name = string("op_455_to_fp16"), val = fp16(0x1p-23)]; - tensor var_456_cast_fp16 = add(x = var_454_cast_fp16, y = var_455_to_fp16)[name = string("op_456_cast_fp16")]; - fp32 var_457_epsilon_0 = const()[name = string("op_457_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_457_cast_fp16 = rsqrt(epsilon = var_457_epsilon_0, x = var_456_cast_fp16)[name = string("op_457_cast_fp16")]; - tensor var_458_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_457_cast_fp16)[name = string("op_458_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(8554560)))]; - tensor var_459_cast_fp16 = mul(x = var_458_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_459_cast_fp16")]; - tensor var_462_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_462_cast_fp16")]; - tensor var_464_axes_0 = const()[name = string("op_464_axes_0"), val = tensor([-1])]; - bool var_464_keep_dims_0 = const()[name = string("op_464_keep_dims_0"), val = bool(true)]; - tensor var_464_cast_fp16 = reduce_mean(axes = var_464_axes_0, keep_dims = var_464_keep_dims_0, x = var_462_cast_fp16)[name = string("op_464_cast_fp16")]; - fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1p-23)]; - tensor var_466_cast_fp16 = add(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("op_466_cast_fp16")]; - fp32 var_467_epsilon_0 = const()[name = string("op_467_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_467_cast_fp16 = rsqrt(epsilon = var_467_epsilon_0, x = var_466_cast_fp16)[name = string("op_467_cast_fp16")]; - tensor var_468_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_467_cast_fp16)[name = string("op_468_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(8554752)))]; - tensor var_469_cast_fp16 = mul(x = var_468_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_469_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_469_cast_fp16)[name = string("transpose_151")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_459_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_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, 1120, 512])]; - tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; - tensor input_63_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("input_63_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(8554944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8817152))))[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(8818240)))]; - tensor linear_17_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_63_cast_fp16)[name = string("linear_17_cast_fp16")]; - tensor var_487_cast_fp16 = mul(x = var_426_cast_fp16, y = linear_17_cast_fp16)[name = string("op_487_cast_fp16")]; - tensor input_67_cast_fp16 = add(x = input_61_cast_fp16, y = var_487_cast_fp16)[name = string("input_67_cast_fp16")]; - tensor var_489_axes_0 = const()[name = string("op_489_axes_0"), val = tensor([1])]; - tensor var_489_cast_fp16 = expand_dims(axes = var_489_axes_0, x = var_420_cast_fp16_5)[name = string("op_489_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_69_to_fp16, x = input_67_cast_fp16)[name = string("x_13_cast_fp16")]; - tensor var_492_axes_0 = const()[name = string("op_492_axes_0"), val = tensor([1])]; - tensor var_492_cast_fp16 = expand_dims(axes = var_492_axes_0, x = var_420_cast_fp16_4)[name = string("op_492_cast_fp16")]; - fp16 var_493_to_fp16 = const()[name = string("op_493_to_fp16"), val = fp16(0x1p+0)]; - tensor var_494_cast_fp16 = add(x = var_492_cast_fp16, y = var_493_to_fp16)[name = string("op_494_cast_fp16")]; - tensor var_495_cast_fp16 = mul(x = x_13_cast_fp16, y = var_494_cast_fp16)[name = string("op_495_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_420_cast_fp16_3)[name = string("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = var_495_cast_fp16, y = var_496_cast_fp16)[name = string("input_69_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(8819328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867968))))[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(9872128)))]; - tensor linear_18_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_69_cast_fp16)[name = string("linear_18_cast_fp16")]; - string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")]; - tensor input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = linear_18_cast_fp16)[name = string("input_73_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(9876288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10924928))))[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(10926016)))]; - tensor linear_19_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_73_cast_fp16)[name = string("linear_19_cast_fp16")]; - tensor var_509_cast_fp16 = mul(x = var_489_cast_fp16, y = linear_19_cast_fp16)[name = string("op_509_cast_fp16")]; - tensor input_81_cast_fp16 = add(x = input_67_cast_fp16, y = var_509_cast_fp16)[name = string("input_81_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(10927104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12500032))))[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(12506240)))]; - tensor linear_20_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_39_cast_fp16)[name = string("linear_20_cast_fp16")]; - tensor var_518_split_sizes_0 = const()[name = string("op_518_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_518_axis_0 = const()[name = string("op_518_axis_0"), val = int32(-1)]; - tensor var_518_cast_fp16_0, tensor var_518_cast_fp16_1, tensor var_518_cast_fp16_2, tensor var_518_cast_fp16_3, tensor var_518_cast_fp16_4, tensor var_518_cast_fp16_5 = split(axis = var_518_axis_0, split_sizes = var_518_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_518_cast_fp16")]; - tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; - tensor var_524_cast_fp16 = expand_dims(axes = var_524_axes_0, x = var_518_cast_fp16_2)[name = string("op_524_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_69_to_fp16, x = input_81_cast_fp16)[name = string("x_15_cast_fp16")]; - tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([1])]; - tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = var_518_cast_fp16_1)[name = string("op_527_cast_fp16")]; - fp16 var_528_to_fp16 = const()[name = string("op_528_to_fp16"), val = fp16(0x1p+0)]; - tensor var_529_cast_fp16 = add(x = var_527_cast_fp16, y = var_528_to_fp16)[name = string("op_529_cast_fp16")]; - tensor var_530_cast_fp16 = mul(x = x_15_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_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_518_cast_fp16_0)[name = string("op_531_cast_fp16")]; - tensor x_17_cast_fp16 = add(x = var_530_cast_fp16, y = var_531_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(12512448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13298944))))[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(13302080)))]; - tensor linear_21_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_21_cast_fp16")]; - tensor var_543 = const()[name = string("op_543"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_5_cast_fp16 = reshape(shape = var_543, x = linear_21_cast_fp16)[name = string("qkv_5_cast_fp16")]; - tensor var_545_split_sizes_0 = const()[name = string("op_545_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_545_axis_0 = const()[name = string("op_545_axis_0"), val = int32(2)]; - tensor var_545_cast_fp16_0, tensor var_545_cast_fp16_1, tensor var_545_cast_fp16_2 = split(axis = var_545_axis_0, split_sizes = var_545_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_545_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_545_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_545_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_545_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; - tensor var_550_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_550_cast_fp16")]; - tensor var_552_axes_0 = const()[name = string("op_552_axes_0"), val = tensor([-1])]; - bool var_552_keep_dims_0 = const()[name = string("op_552_keep_dims_0"), val = bool(true)]; - tensor var_552_cast_fp16 = reduce_mean(axes = var_552_axes_0, keep_dims = var_552_keep_dims_0, x = var_550_cast_fp16)[name = string("op_552_cast_fp16")]; - fp16 var_553_to_fp16 = const()[name = string("op_553_to_fp16"), val = fp16(0x1p-23)]; - tensor var_554_cast_fp16 = add(x = var_552_cast_fp16, y = var_553_to_fp16)[name = string("op_554_cast_fp16")]; - fp32 var_555_epsilon_0 = const()[name = string("op_555_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_555_cast_fp16 = rsqrt(epsilon = var_555_epsilon_0, x = var_554_cast_fp16)[name = string("op_555_cast_fp16")]; - tensor var_556_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_555_cast_fp16)[name = string("op_556_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(13305216)))]; - tensor var_557_cast_fp16 = mul(x = var_556_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_557_cast_fp16")]; - tensor var_560_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_560_cast_fp16")]; - tensor var_562_axes_0 = const()[name = string("op_562_axes_0"), val = tensor([-1])]; - bool var_562_keep_dims_0 = const()[name = string("op_562_keep_dims_0"), val = bool(true)]; - tensor var_562_cast_fp16 = reduce_mean(axes = var_562_axes_0, keep_dims = var_562_keep_dims_0, x = var_560_cast_fp16)[name = string("op_562_cast_fp16")]; - fp16 var_563_to_fp16 = const()[name = string("op_563_to_fp16"), val = fp16(0x1p-23)]; - tensor var_564_cast_fp16 = add(x = var_562_cast_fp16, y = var_563_to_fp16)[name = string("op_564_cast_fp16")]; - fp32 var_565_epsilon_0 = const()[name = string("op_565_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_565_cast_fp16 = rsqrt(epsilon = var_565_epsilon_0, x = var_564_cast_fp16)[name = string("op_565_cast_fp16")]; - tensor var_566_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_565_cast_fp16)[name = string("op_566_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(13305408)))]; - tensor var_567_cast_fp16 = mul(x = var_566_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_567_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_567_cast_fp16)[name = string("transpose_147")]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_557_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_578_perm_0 = const()[name = string("op_578_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_579 = const()[name = string("op_579"), val = tensor([1, 1120, 512])]; - tensor var_578_cast_fp16 = transpose(perm = var_578_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; - tensor input_83_cast_fp16 = reshape(shape = var_579, x = var_578_cast_fp16)[name = string("input_83_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(13305600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13567808))))[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(13568896)))]; - tensor linear_22_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_83_cast_fp16)[name = string("linear_22_cast_fp16")]; - tensor var_585_cast_fp16 = mul(x = var_524_cast_fp16, y = linear_22_cast_fp16)[name = string("op_585_cast_fp16")]; - tensor input_87_cast_fp16 = add(x = input_81_cast_fp16, y = var_585_cast_fp16)[name = string("input_87_cast_fp16")]; - tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([1])]; - tensor var_587_cast_fp16 = expand_dims(axes = var_587_axes_0, x = var_518_cast_fp16_5)[name = string("op_587_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_69_to_fp16, x = input_87_cast_fp16)[name = string("x_19_cast_fp16")]; - tensor var_590_axes_0 = const()[name = string("op_590_axes_0"), val = tensor([1])]; - tensor var_590_cast_fp16 = expand_dims(axes = var_590_axes_0, x = var_518_cast_fp16_4)[name = string("op_590_cast_fp16")]; - fp16 var_591_to_fp16 = const()[name = string("op_591_to_fp16"), val = fp16(0x1p+0)]; - tensor var_592_cast_fp16 = add(x = var_590_cast_fp16, y = var_591_to_fp16)[name = string("op_592_cast_fp16")]; - tensor var_593_cast_fp16 = mul(x = x_19_cast_fp16, y = var_592_cast_fp16)[name = string("op_593_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_518_cast_fp16_3)[name = string("op_594_cast_fp16")]; - tensor input_89_cast_fp16 = add(x = var_593_cast_fp16, y = var_594_cast_fp16)[name = string("input_89_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(13569984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14618624))))[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(14622784)))]; - tensor linear_23_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_89_cast_fp16)[name = string("linear_23_cast_fp16")]; - string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")]; - tensor input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = linear_23_cast_fp16)[name = string("input_93_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(14626944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15675584))))[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(15676672)))]; - tensor linear_24_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_93_cast_fp16)[name = string("linear_24_cast_fp16")]; - tensor var_607_cast_fp16 = mul(x = var_587_cast_fp16, y = linear_24_cast_fp16)[name = string("op_607_cast_fp16")]; - tensor input_101_cast_fp16 = add(x = input_87_cast_fp16, y = var_607_cast_fp16)[name = string("input_101_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(15677760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17250688))))[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(17256896)))]; - tensor linear_25_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_39_cast_fp16)[name = string("linear_25_cast_fp16")]; - tensor var_616_split_sizes_0 = const()[name = string("op_616_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_616_axis_0 = const()[name = string("op_616_axis_0"), val = int32(-1)]; - tensor var_616_cast_fp16_0, tensor var_616_cast_fp16_1, tensor var_616_cast_fp16_2, tensor var_616_cast_fp16_3, tensor var_616_cast_fp16_4, tensor var_616_cast_fp16_5 = split(axis = var_616_axis_0, split_sizes = var_616_split_sizes_0, x = linear_25_cast_fp16)[name = string("op_616_cast_fp16")]; - tensor var_622_axes_0 = const()[name = string("op_622_axes_0"), val = tensor([1])]; - tensor var_622_cast_fp16 = expand_dims(axes = var_622_axes_0, x = var_616_cast_fp16_2)[name = string("op_622_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_69_to_fp16, x = input_101_cast_fp16)[name = string("x_21_cast_fp16")]; - tensor var_625_axes_0 = const()[name = string("op_625_axes_0"), val = tensor([1])]; - tensor var_625_cast_fp16 = expand_dims(axes = var_625_axes_0, x = var_616_cast_fp16_1)[name = string("op_625_cast_fp16")]; - fp16 var_626_to_fp16 = const()[name = string("op_626_to_fp16"), val = fp16(0x1p+0)]; - tensor var_627_cast_fp16 = add(x = var_625_cast_fp16, y = var_626_to_fp16)[name = string("op_627_cast_fp16")]; - tensor var_628_cast_fp16 = mul(x = x_21_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_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_616_cast_fp16_0)[name = string("op_629_cast_fp16")]; - tensor x_23_cast_fp16 = add(x = var_628_cast_fp16, y = var_629_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(17263104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18049600))))[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(18052736)))]; - tensor linear_26_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_26_cast_fp16")]; - tensor var_641 = const()[name = string("op_641"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_7_cast_fp16 = reshape(shape = var_641, x = linear_26_cast_fp16)[name = string("qkv_7_cast_fp16")]; - tensor var_643_split_sizes_0 = const()[name = string("op_643_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_643_axis_0 = const()[name = string("op_643_axis_0"), val = int32(2)]; - tensor var_643_cast_fp16_0, tensor var_643_cast_fp16_1, tensor var_643_cast_fp16_2 = split(axis = var_643_axis_0, split_sizes = var_643_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_643_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_643_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_643_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_643_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; - tensor var_648_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_648_cast_fp16")]; - tensor var_650_axes_0 = const()[name = string("op_650_axes_0"), val = tensor([-1])]; - bool var_650_keep_dims_0 = const()[name = string("op_650_keep_dims_0"), val = bool(true)]; - tensor var_650_cast_fp16 = reduce_mean(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = string("op_650_cast_fp16")]; - fp16 var_651_to_fp16 = const()[name = string("op_651_to_fp16"), val = fp16(0x1p-23)]; - tensor var_652_cast_fp16 = add(x = var_650_cast_fp16, y = var_651_to_fp16)[name = string("op_652_cast_fp16")]; - fp32 var_653_epsilon_0 = const()[name = string("op_653_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_653_cast_fp16 = rsqrt(epsilon = var_653_epsilon_0, x = var_652_cast_fp16)[name = string("op_653_cast_fp16")]; - tensor var_654_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_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(18055872)))]; - tensor var_655_cast_fp16 = mul(x = var_654_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_655_cast_fp16")]; - tensor var_658_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_658_cast_fp16")]; - tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; - bool var_660_keep_dims_0 = const()[name = string("op_660_keep_dims_0"), val = bool(true)]; - tensor var_660_cast_fp16 = reduce_mean(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = string("op_660_cast_fp16")]; - fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-23)]; - tensor var_662_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("op_662_cast_fp16")]; - fp32 var_663_epsilon_0 = const()[name = string("op_663_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_663_cast_fp16 = rsqrt(epsilon = var_663_epsilon_0, x = var_662_cast_fp16)[name = string("op_663_cast_fp16")]; - tensor var_664_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_663_cast_fp16)[name = string("op_664_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(18056064)))]; - tensor var_665_cast_fp16 = mul(x = var_664_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_665_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_665_cast_fp16)[name = string("transpose_143")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_655_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_676_perm_0 = const()[name = string("op_676_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_677 = const()[name = string("op_677"), val = tensor([1, 1120, 512])]; - tensor var_676_cast_fp16 = transpose(perm = var_676_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; - tensor input_103_cast_fp16 = reshape(shape = var_677, x = var_676_cast_fp16)[name = string("input_103_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(18056256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18318464))))[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(18319552)))]; - tensor linear_27_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_103_cast_fp16)[name = string("linear_27_cast_fp16")]; - tensor var_683_cast_fp16 = mul(x = var_622_cast_fp16, y = linear_27_cast_fp16)[name = string("op_683_cast_fp16")]; - tensor input_107_cast_fp16 = add(x = input_101_cast_fp16, y = var_683_cast_fp16)[name = string("input_107_cast_fp16")]; - tensor var_685_axes_0 = const()[name = string("op_685_axes_0"), val = tensor([1])]; - tensor var_685_cast_fp16 = expand_dims(axes = var_685_axes_0, x = var_616_cast_fp16_5)[name = string("op_685_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_69_to_fp16, x = input_107_cast_fp16)[name = string("x_25_cast_fp16")]; - tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([1])]; - tensor var_688_cast_fp16 = expand_dims(axes = var_688_axes_0, x = var_616_cast_fp16_4)[name = string("op_688_cast_fp16")]; - fp16 var_689_to_fp16 = const()[name = string("op_689_to_fp16"), val = fp16(0x1p+0)]; - tensor var_690_cast_fp16 = add(x = var_688_cast_fp16, y = var_689_to_fp16)[name = string("op_690_cast_fp16")]; - tensor var_691_cast_fp16 = mul(x = x_25_cast_fp16, y = var_690_cast_fp16)[name = string("op_691_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_616_cast_fp16_3)[name = string("op_692_cast_fp16")]; - tensor input_109_cast_fp16 = add(x = var_691_cast_fp16, y = var_692_cast_fp16)[name = string("input_109_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(18320640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19369280))))[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(19373440)))]; - tensor linear_28_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_109_cast_fp16)[name = string("linear_28_cast_fp16")]; - string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; - tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_28_cast_fp16)[name = string("input_113_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(19377600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20426240))))[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(20427328)))]; - tensor linear_29_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_113_cast_fp16)[name = string("linear_29_cast_fp16")]; - tensor var_705_cast_fp16 = mul(x = var_685_cast_fp16, y = linear_29_cast_fp16)[name = string("op_705_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_107_cast_fp16, y = var_705_cast_fp16)[name = string("input_121_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(20428416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001344))))[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(22007552)))]; - tensor linear_30_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_39_cast_fp16)[name = string("linear_30_cast_fp16")]; - tensor var_714_split_sizes_0 = const()[name = string("op_714_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_714_axis_0 = const()[name = string("op_714_axis_0"), val = int32(-1)]; - tensor var_714_cast_fp16_0, tensor var_714_cast_fp16_1, tensor var_714_cast_fp16_2, tensor var_714_cast_fp16_3, tensor var_714_cast_fp16_4, tensor var_714_cast_fp16_5 = split(axis = var_714_axis_0, split_sizes = var_714_split_sizes_0, x = linear_30_cast_fp16)[name = string("op_714_cast_fp16")]; - tensor var_720_axes_0 = const()[name = string("op_720_axes_0"), val = tensor([1])]; - tensor var_720_cast_fp16 = expand_dims(axes = var_720_axes_0, x = var_714_cast_fp16_2)[name = string("op_720_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_69_to_fp16, x = input_121_cast_fp16)[name = string("x_27_cast_fp16")]; - tensor var_723_axes_0 = const()[name = string("op_723_axes_0"), val = tensor([1])]; - tensor var_723_cast_fp16 = expand_dims(axes = var_723_axes_0, x = var_714_cast_fp16_1)[name = string("op_723_cast_fp16")]; - fp16 var_724_to_fp16 = const()[name = string("op_724_to_fp16"), val = fp16(0x1p+0)]; - tensor var_725_cast_fp16 = add(x = var_723_cast_fp16, y = var_724_to_fp16)[name = string("op_725_cast_fp16")]; - tensor var_726_cast_fp16 = mul(x = x_27_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_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_714_cast_fp16_0)[name = string("op_727_cast_fp16")]; - tensor x_29_cast_fp16 = add(x = var_726_cast_fp16, y = var_727_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(22013760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22800256))))[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(22803392)))]; - tensor linear_31_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_31_cast_fp16")]; - tensor var_739 = const()[name = string("op_739"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_9_cast_fp16 = reshape(shape = var_739, x = linear_31_cast_fp16)[name = string("qkv_9_cast_fp16")]; - tensor var_741_split_sizes_0 = const()[name = string("op_741_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_741_axis_0 = const()[name = string("op_741_axis_0"), val = int32(2)]; - tensor var_741_cast_fp16_0, tensor var_741_cast_fp16_1, tensor var_741_cast_fp16_2 = split(axis = var_741_axis_0, split_sizes = var_741_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_741_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_741_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_741_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_741_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; - tensor var_746_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_746_cast_fp16")]; - tensor var_748_axes_0 = const()[name = string("op_748_axes_0"), val = tensor([-1])]; - bool var_748_keep_dims_0 = const()[name = string("op_748_keep_dims_0"), val = bool(true)]; - tensor var_748_cast_fp16 = reduce_mean(axes = var_748_axes_0, keep_dims = var_748_keep_dims_0, x = var_746_cast_fp16)[name = string("op_748_cast_fp16")]; - fp16 var_749_to_fp16 = const()[name = string("op_749_to_fp16"), val = fp16(0x1p-23)]; - tensor var_750_cast_fp16 = add(x = var_748_cast_fp16, y = var_749_to_fp16)[name = string("op_750_cast_fp16")]; - fp32 var_751_epsilon_0 = const()[name = string("op_751_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_751_cast_fp16 = rsqrt(epsilon = var_751_epsilon_0, x = var_750_cast_fp16)[name = string("op_751_cast_fp16")]; - tensor var_752_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_751_cast_fp16)[name = string("op_752_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(22806528)))]; - tensor var_753_cast_fp16 = mul(x = var_752_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_753_cast_fp16")]; - tensor var_756_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_756_cast_fp16")]; - tensor var_758_axes_0 = const()[name = string("op_758_axes_0"), val = tensor([-1])]; - bool var_758_keep_dims_0 = const()[name = string("op_758_keep_dims_0"), val = bool(true)]; - tensor var_758_cast_fp16 = reduce_mean(axes = var_758_axes_0, keep_dims = var_758_keep_dims_0, x = var_756_cast_fp16)[name = string("op_758_cast_fp16")]; - fp16 var_759_to_fp16 = const()[name = string("op_759_to_fp16"), val = fp16(0x1p-23)]; - tensor var_760_cast_fp16 = add(x = var_758_cast_fp16, y = var_759_to_fp16)[name = string("op_760_cast_fp16")]; - fp32 var_761_epsilon_0 = const()[name = string("op_761_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_761_cast_fp16 = rsqrt(epsilon = var_761_epsilon_0, x = var_760_cast_fp16)[name = string("op_761_cast_fp16")]; - tensor var_762_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_761_cast_fp16)[name = string("op_762_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(22806720)))]; - tensor var_763_cast_fp16 = mul(x = var_762_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_763_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_763_cast_fp16)[name = string("transpose_139")]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_753_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_774_perm_0 = const()[name = string("op_774_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_775 = const()[name = string("op_775"), val = tensor([1, 1120, 512])]; - tensor var_774_cast_fp16 = transpose(perm = var_774_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; - tensor input_123_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("input_123_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(22806912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23069120))))[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(23070208)))]; - tensor linear_32_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_123_cast_fp16)[name = string("linear_32_cast_fp16")]; - tensor var_781_cast_fp16 = mul(x = var_720_cast_fp16, y = linear_32_cast_fp16)[name = string("op_781_cast_fp16")]; - tensor input_127_cast_fp16 = add(x = input_121_cast_fp16, y = var_781_cast_fp16)[name = string("input_127_cast_fp16")]; - tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([1])]; - tensor var_783_cast_fp16 = expand_dims(axes = var_783_axes_0, x = var_714_cast_fp16_5)[name = string("op_783_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_69_to_fp16, x = input_127_cast_fp16)[name = string("x_31_cast_fp16")]; - tensor var_786_axes_0 = const()[name = string("op_786_axes_0"), val = tensor([1])]; - tensor var_786_cast_fp16 = expand_dims(axes = var_786_axes_0, x = var_714_cast_fp16_4)[name = string("op_786_cast_fp16")]; - fp16 var_787_to_fp16 = const()[name = string("op_787_to_fp16"), val = fp16(0x1p+0)]; - tensor var_788_cast_fp16 = add(x = var_786_cast_fp16, y = var_787_to_fp16)[name = string("op_788_cast_fp16")]; - tensor var_789_cast_fp16 = mul(x = x_31_cast_fp16, y = var_788_cast_fp16)[name = string("op_789_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_714_cast_fp16_3)[name = string("op_790_cast_fp16")]; - tensor input_129_cast_fp16 = add(x = var_789_cast_fp16, y = var_790_cast_fp16)[name = string("input_129_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(23071296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119936))))[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(24124096)))]; - tensor linear_33_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_129_cast_fp16)[name = string("linear_33_cast_fp16")]; - string input_133_mode_0 = const()[name = string("input_133_mode_0"), val = string("EXACT")]; - tensor input_133_cast_fp16 = gelu(mode = input_133_mode_0, x = linear_33_cast_fp16)[name = string("input_133_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(24128256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176896))))[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(25177984)))]; - tensor linear_34_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_133_cast_fp16)[name = string("linear_34_cast_fp16")]; - tensor var_803_cast_fp16 = mul(x = var_783_cast_fp16, y = linear_34_cast_fp16)[name = string("op_803_cast_fp16")]; - tensor input_141_cast_fp16 = add(x = input_127_cast_fp16, y = var_803_cast_fp16)[name = string("input_141_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(25179072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26752000))))[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(26758208)))]; - tensor linear_35_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_39_cast_fp16)[name = string("linear_35_cast_fp16")]; - tensor var_812_split_sizes_0 = const()[name = string("op_812_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_812_axis_0 = const()[name = string("op_812_axis_0"), val = int32(-1)]; - tensor var_812_cast_fp16_0, tensor var_812_cast_fp16_1, tensor var_812_cast_fp16_2, tensor var_812_cast_fp16_3, tensor var_812_cast_fp16_4, tensor var_812_cast_fp16_5 = split(axis = var_812_axis_0, split_sizes = var_812_split_sizes_0, x = linear_35_cast_fp16)[name = string("op_812_cast_fp16")]; - tensor var_818_axes_0 = const()[name = string("op_818_axes_0"), val = tensor([1])]; - tensor var_818_cast_fp16 = expand_dims(axes = var_818_axes_0, x = var_812_cast_fp16_2)[name = string("op_818_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_69_to_fp16, x = input_141_cast_fp16)[name = string("x_33_cast_fp16")]; - tensor var_821_axes_0 = const()[name = string("op_821_axes_0"), val = tensor([1])]; - tensor var_821_cast_fp16 = expand_dims(axes = var_821_axes_0, x = var_812_cast_fp16_1)[name = string("op_821_cast_fp16")]; - fp16 var_822_to_fp16 = const()[name = string("op_822_to_fp16"), val = fp16(0x1p+0)]; - tensor var_823_cast_fp16 = add(x = var_821_cast_fp16, y = var_822_to_fp16)[name = string("op_823_cast_fp16")]; - tensor var_824_cast_fp16 = mul(x = x_33_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_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_812_cast_fp16_0)[name = string("op_825_cast_fp16")]; - tensor x_35_cast_fp16 = add(x = var_824_cast_fp16, y = var_825_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(26764416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27550912))))[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(27554048)))]; - tensor linear_36_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_36_cast_fp16")]; - tensor var_837 = const()[name = string("op_837"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_11_cast_fp16 = reshape(shape = var_837, x = linear_36_cast_fp16)[name = string("qkv_11_cast_fp16")]; - tensor var_839_split_sizes_0 = const()[name = string("op_839_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_839_axis_0 = const()[name = string("op_839_axis_0"), val = int32(2)]; - tensor var_839_cast_fp16_0, tensor var_839_cast_fp16_1, tensor var_839_cast_fp16_2 = split(axis = var_839_axis_0, split_sizes = var_839_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_839_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_839_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_839_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_839_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; - tensor var_844_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_844_cast_fp16")]; - tensor var_846_axes_0 = const()[name = string("op_846_axes_0"), val = tensor([-1])]; - bool var_846_keep_dims_0 = const()[name = string("op_846_keep_dims_0"), val = bool(true)]; - tensor var_846_cast_fp16 = reduce_mean(axes = var_846_axes_0, keep_dims = var_846_keep_dims_0, x = var_844_cast_fp16)[name = string("op_846_cast_fp16")]; - fp16 var_847_to_fp16 = const()[name = string("op_847_to_fp16"), val = fp16(0x1p-23)]; - tensor var_848_cast_fp16 = add(x = var_846_cast_fp16, y = var_847_to_fp16)[name = string("op_848_cast_fp16")]; - fp32 var_849_epsilon_0 = const()[name = string("op_849_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_849_cast_fp16 = rsqrt(epsilon = var_849_epsilon_0, x = var_848_cast_fp16)[name = string("op_849_cast_fp16")]; - tensor var_850_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_849_cast_fp16)[name = string("op_850_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(27557184)))]; - tensor var_851_cast_fp16 = mul(x = var_850_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_851_cast_fp16")]; - tensor var_854_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_854_cast_fp16")]; - tensor var_856_axes_0 = const()[name = string("op_856_axes_0"), val = tensor([-1])]; - bool var_856_keep_dims_0 = const()[name = string("op_856_keep_dims_0"), val = bool(true)]; - tensor var_856_cast_fp16 = reduce_mean(axes = var_856_axes_0, keep_dims = var_856_keep_dims_0, x = var_854_cast_fp16)[name = string("op_856_cast_fp16")]; - fp16 var_857_to_fp16 = const()[name = string("op_857_to_fp16"), val = fp16(0x1p-23)]; - tensor var_858_cast_fp16 = add(x = var_856_cast_fp16, y = var_857_to_fp16)[name = string("op_858_cast_fp16")]; - fp32 var_859_epsilon_0 = const()[name = string("op_859_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_859_cast_fp16 = rsqrt(epsilon = var_859_epsilon_0, x = var_858_cast_fp16)[name = string("op_859_cast_fp16")]; - tensor var_860_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_859_cast_fp16)[name = string("op_860_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(27557376)))]; - tensor var_861_cast_fp16 = mul(x = var_860_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_861_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_861_cast_fp16)[name = string("transpose_135")]; - tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_851_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_872_perm_0 = const()[name = string("op_872_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_873 = const()[name = string("op_873"), val = tensor([1, 1120, 512])]; - tensor var_872_cast_fp16 = transpose(perm = var_872_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; - tensor input_143_cast_fp16 = reshape(shape = var_873, x = var_872_cast_fp16)[name = string("input_143_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(27557568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27819776))))[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(27820864)))]; - tensor linear_37_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_143_cast_fp16)[name = string("linear_37_cast_fp16")]; - tensor var_879_cast_fp16 = mul(x = var_818_cast_fp16, y = linear_37_cast_fp16)[name = string("op_879_cast_fp16")]; - tensor input_147_cast_fp16 = add(x = input_141_cast_fp16, y = var_879_cast_fp16)[name = string("input_147_cast_fp16")]; - tensor var_881_axes_0 = const()[name = string("op_881_axes_0"), val = tensor([1])]; - tensor var_881_cast_fp16 = expand_dims(axes = var_881_axes_0, x = var_812_cast_fp16_5)[name = string("op_881_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_69_to_fp16, x = input_147_cast_fp16)[name = string("x_37_cast_fp16")]; - tensor var_884_axes_0 = const()[name = string("op_884_axes_0"), val = tensor([1])]; - tensor var_884_cast_fp16 = expand_dims(axes = var_884_axes_0, x = var_812_cast_fp16_4)[name = string("op_884_cast_fp16")]; - fp16 var_885_to_fp16 = const()[name = string("op_885_to_fp16"), val = fp16(0x1p+0)]; - tensor var_886_cast_fp16 = add(x = var_884_cast_fp16, y = var_885_to_fp16)[name = string("op_886_cast_fp16")]; - tensor var_887_cast_fp16 = mul(x = x_37_cast_fp16, y = var_886_cast_fp16)[name = string("op_887_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_812_cast_fp16_3)[name = string("op_888_cast_fp16")]; - tensor input_149_cast_fp16 = add(x = var_887_cast_fp16, y = var_888_cast_fp16)[name = string("input_149_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(27821952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28870592))))[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(28874752)))]; - tensor linear_38_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_149_cast_fp16)[name = string("linear_38_cast_fp16")]; - string input_153_mode_0 = const()[name = string("input_153_mode_0"), val = string("EXACT")]; - tensor input_153_cast_fp16 = gelu(mode = input_153_mode_0, x = linear_38_cast_fp16)[name = string("input_153_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(28878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29927552))))[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(29928640)))]; - tensor linear_39_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_153_cast_fp16)[name = string("linear_39_cast_fp16")]; - tensor var_901_cast_fp16 = mul(x = var_881_cast_fp16, y = linear_39_cast_fp16)[name = string("op_901_cast_fp16")]; - tensor input_161_cast_fp16 = add(x = input_147_cast_fp16, y = var_901_cast_fp16)[name = string("input_161_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(29929728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31502656))))[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(31508864)))]; - tensor linear_40_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_39_cast_fp16)[name = string("linear_40_cast_fp16")]; - tensor var_910_split_sizes_0 = const()[name = string("op_910_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_910_axis_0 = const()[name = string("op_910_axis_0"), val = int32(-1)]; - tensor var_910_cast_fp16_0, tensor var_910_cast_fp16_1, tensor var_910_cast_fp16_2, tensor var_910_cast_fp16_3, tensor var_910_cast_fp16_4, tensor var_910_cast_fp16_5 = split(axis = var_910_axis_0, split_sizes = var_910_split_sizes_0, x = linear_40_cast_fp16)[name = string("op_910_cast_fp16")]; - tensor var_916_axes_0 = const()[name = string("op_916_axes_0"), val = tensor([1])]; - tensor var_916_cast_fp16 = expand_dims(axes = var_916_axes_0, x = var_910_cast_fp16_2)[name = string("op_916_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_69_to_fp16, x = input_161_cast_fp16)[name = string("x_39_cast_fp16")]; - tensor var_919_axes_0 = const()[name = string("op_919_axes_0"), val = tensor([1])]; - tensor var_919_cast_fp16 = expand_dims(axes = var_919_axes_0, x = var_910_cast_fp16_1)[name = string("op_919_cast_fp16")]; - fp16 var_920_to_fp16 = const()[name = string("op_920_to_fp16"), val = fp16(0x1p+0)]; - tensor var_921_cast_fp16 = add(x = var_919_cast_fp16, y = var_920_to_fp16)[name = string("op_921_cast_fp16")]; - tensor var_922_cast_fp16 = mul(x = x_39_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_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_910_cast_fp16_0)[name = string("op_923_cast_fp16")]; - tensor x_41_cast_fp16 = add(x = var_922_cast_fp16, y = var_923_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(31515072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32301568))))[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(32304704)))]; - tensor linear_41_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_41_cast_fp16")]; - tensor var_935 = const()[name = string("op_935"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_13_cast_fp16 = reshape(shape = var_935, x = linear_41_cast_fp16)[name = string("qkv_13_cast_fp16")]; - tensor var_937_split_sizes_0 = const()[name = string("op_937_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_937_axis_0 = const()[name = string("op_937_axis_0"), val = int32(2)]; - tensor var_937_cast_fp16_0, tensor var_937_cast_fp16_1, tensor var_937_cast_fp16_2 = split(axis = var_937_axis_0, split_sizes = var_937_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_937_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_937_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_937_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_937_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; - tensor var_942_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_942_cast_fp16")]; - tensor var_944_axes_0 = const()[name = string("op_944_axes_0"), val = tensor([-1])]; - bool var_944_keep_dims_0 = const()[name = string("op_944_keep_dims_0"), val = bool(true)]; - tensor var_944_cast_fp16 = reduce_mean(axes = var_944_axes_0, keep_dims = var_944_keep_dims_0, x = var_942_cast_fp16)[name = string("op_944_cast_fp16")]; - fp16 var_945_to_fp16 = const()[name = string("op_945_to_fp16"), val = fp16(0x1p-23)]; - tensor var_946_cast_fp16 = add(x = var_944_cast_fp16, y = var_945_to_fp16)[name = string("op_946_cast_fp16")]; - fp32 var_947_epsilon_0 = const()[name = string("op_947_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_947_cast_fp16 = rsqrt(epsilon = var_947_epsilon_0, x = var_946_cast_fp16)[name = string("op_947_cast_fp16")]; - tensor var_948_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_947_cast_fp16)[name = string("op_948_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(32307840)))]; - tensor var_949_cast_fp16 = mul(x = var_948_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_949_cast_fp16")]; - tensor var_952_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_952_cast_fp16")]; - tensor var_954_axes_0 = const()[name = string("op_954_axes_0"), val = tensor([-1])]; - bool var_954_keep_dims_0 = const()[name = string("op_954_keep_dims_0"), val = bool(true)]; - tensor var_954_cast_fp16 = reduce_mean(axes = var_954_axes_0, keep_dims = var_954_keep_dims_0, x = var_952_cast_fp16)[name = string("op_954_cast_fp16")]; - fp16 var_955_to_fp16 = const()[name = string("op_955_to_fp16"), val = fp16(0x1p-23)]; - tensor var_956_cast_fp16 = add(x = var_954_cast_fp16, y = var_955_to_fp16)[name = string("op_956_cast_fp16")]; - fp32 var_957_epsilon_0 = const()[name = string("op_957_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_957_cast_fp16 = rsqrt(epsilon = var_957_epsilon_0, x = var_956_cast_fp16)[name = string("op_957_cast_fp16")]; - tensor var_958_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_957_cast_fp16)[name = string("op_958_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(32308032)))]; - tensor var_959_cast_fp16 = mul(x = var_958_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_959_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_959_cast_fp16)[name = string("transpose_131")]; - tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_949_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_970_perm_0 = const()[name = string("op_970_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_971 = const()[name = string("op_971"), val = tensor([1, 1120, 512])]; - tensor var_970_cast_fp16 = transpose(perm = var_970_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; - tensor input_163_cast_fp16 = reshape(shape = var_971, x = var_970_cast_fp16)[name = string("input_163_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(32308224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32570432))))[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(32571520)))]; - tensor linear_42_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_163_cast_fp16)[name = string("linear_42_cast_fp16")]; - tensor var_977_cast_fp16 = mul(x = var_916_cast_fp16, y = linear_42_cast_fp16)[name = string("op_977_cast_fp16")]; - tensor input_167_cast_fp16 = add(x = input_161_cast_fp16, y = var_977_cast_fp16)[name = string("input_167_cast_fp16")]; - tensor var_979_axes_0 = const()[name = string("op_979_axes_0"), val = tensor([1])]; - tensor var_979_cast_fp16 = expand_dims(axes = var_979_axes_0, x = var_910_cast_fp16_5)[name = string("op_979_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_69_to_fp16, x = input_167_cast_fp16)[name = string("x_43_cast_fp16")]; - tensor var_982_axes_0 = const()[name = string("op_982_axes_0"), val = tensor([1])]; - tensor var_982_cast_fp16 = expand_dims(axes = var_982_axes_0, x = var_910_cast_fp16_4)[name = string("op_982_cast_fp16")]; - fp16 var_983_to_fp16 = const()[name = string("op_983_to_fp16"), val = fp16(0x1p+0)]; - tensor var_984_cast_fp16 = add(x = var_982_cast_fp16, y = var_983_to_fp16)[name = string("op_984_cast_fp16")]; - tensor var_985_cast_fp16 = mul(x = x_43_cast_fp16, y = var_984_cast_fp16)[name = string("op_985_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_910_cast_fp16_3)[name = string("op_986_cast_fp16")]; - tensor input_169_cast_fp16 = add(x = var_985_cast_fp16, y = var_986_cast_fp16)[name = string("input_169_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(32572608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33621248))))[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(33625408)))]; - tensor linear_43_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_169_cast_fp16)[name = string("linear_43_cast_fp16")]; - string input_173_mode_0 = const()[name = string("input_173_mode_0"), val = string("EXACT")]; - tensor input_173_cast_fp16 = gelu(mode = input_173_mode_0, x = linear_43_cast_fp16)[name = string("input_173_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(33629568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34678208))))[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(34679296)))]; - tensor linear_44_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_173_cast_fp16)[name = string("linear_44_cast_fp16")]; - tensor var_999_cast_fp16 = mul(x = var_979_cast_fp16, y = linear_44_cast_fp16)[name = string("op_999_cast_fp16")]; - tensor input_181_cast_fp16 = add(x = input_167_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_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(34680384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36253312))))[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(36259520)))]; - tensor linear_45_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_39_cast_fp16)[name = string("linear_45_cast_fp16")]; - tensor var_1008_split_sizes_0 = const()[name = string("op_1008_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1008_axis_0 = const()[name = string("op_1008_axis_0"), val = int32(-1)]; - tensor var_1008_cast_fp16_0, tensor var_1008_cast_fp16_1, tensor var_1008_cast_fp16_2, tensor var_1008_cast_fp16_3, tensor var_1008_cast_fp16_4, tensor var_1008_cast_fp16_5 = split(axis = var_1008_axis_0, split_sizes = var_1008_split_sizes_0, x = linear_45_cast_fp16)[name = string("op_1008_cast_fp16")]; - tensor var_1014_axes_0 = const()[name = string("op_1014_axes_0"), val = tensor([1])]; - tensor var_1014_cast_fp16 = expand_dims(axes = var_1014_axes_0, x = var_1008_cast_fp16_2)[name = string("op_1014_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_69_to_fp16, x = input_181_cast_fp16)[name = string("x_45_cast_fp16")]; - tensor var_1017_axes_0 = const()[name = string("op_1017_axes_0"), val = tensor([1])]; - tensor var_1017_cast_fp16 = expand_dims(axes = var_1017_axes_0, x = var_1008_cast_fp16_1)[name = string("op_1017_cast_fp16")]; - fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1019_cast_fp16 = add(x = var_1017_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; - tensor var_1020_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_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_1008_cast_fp16_0)[name = string("op_1021_cast_fp16")]; - tensor x_47_cast_fp16 = add(x = var_1020_cast_fp16, y = var_1021_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(36265728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37052224))))[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(37055360)))]; - tensor linear_46_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_46_cast_fp16")]; - tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_15_cast_fp16 = reshape(shape = var_1033, x = linear_46_cast_fp16)[name = string("qkv_15_cast_fp16")]; - tensor var_1035_split_sizes_0 = const()[name = string("op_1035_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1035_axis_0 = const()[name = string("op_1035_axis_0"), val = int32(2)]; - tensor var_1035_cast_fp16_0, tensor var_1035_cast_fp16_1, tensor var_1035_cast_fp16_2 = split(axis = var_1035_axis_0, split_sizes = var_1035_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1035_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_1035_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_1035_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_1035_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; - tensor var_1040_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1040_cast_fp16")]; - tensor var_1042_axes_0 = const()[name = string("op_1042_axes_0"), val = tensor([-1])]; - bool var_1042_keep_dims_0 = const()[name = string("op_1042_keep_dims_0"), val = bool(true)]; - tensor var_1042_cast_fp16 = reduce_mean(axes = var_1042_axes_0, keep_dims = var_1042_keep_dims_0, x = var_1040_cast_fp16)[name = string("op_1042_cast_fp16")]; - fp16 var_1043_to_fp16 = const()[name = string("op_1043_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1044_cast_fp16 = add(x = var_1042_cast_fp16, y = var_1043_to_fp16)[name = string("op_1044_cast_fp16")]; - fp32 var_1045_epsilon_0 = const()[name = string("op_1045_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1045_cast_fp16 = rsqrt(epsilon = var_1045_epsilon_0, x = var_1044_cast_fp16)[name = string("op_1045_cast_fp16")]; - tensor var_1046_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1045_cast_fp16)[name = string("op_1046_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(37058496)))]; - tensor var_1047_cast_fp16 = mul(x = var_1046_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1047_cast_fp16")]; - tensor var_1050_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1050_cast_fp16")]; - tensor var_1052_axes_0 = const()[name = string("op_1052_axes_0"), val = tensor([-1])]; - bool var_1052_keep_dims_0 = const()[name = string("op_1052_keep_dims_0"), val = bool(true)]; - tensor var_1052_cast_fp16 = reduce_mean(axes = var_1052_axes_0, keep_dims = var_1052_keep_dims_0, x = var_1050_cast_fp16)[name = string("op_1052_cast_fp16")]; - fp16 var_1053_to_fp16 = const()[name = string("op_1053_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1054_cast_fp16 = add(x = var_1052_cast_fp16, y = var_1053_to_fp16)[name = string("op_1054_cast_fp16")]; - fp32 var_1055_epsilon_0 = const()[name = string("op_1055_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1055_cast_fp16 = rsqrt(epsilon = var_1055_epsilon_0, x = var_1054_cast_fp16)[name = string("op_1055_cast_fp16")]; - tensor var_1056_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1055_cast_fp16)[name = string("op_1056_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(37058688)))]; - tensor var_1057_cast_fp16 = mul(x = var_1056_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1057_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_1057_cast_fp16)[name = string("transpose_127")]; - tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1047_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_1068_perm_0 = const()[name = string("op_1068_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1069 = const()[name = string("op_1069"), val = tensor([1, 1120, 512])]; - tensor var_1068_cast_fp16 = transpose(perm = var_1068_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; - tensor input_183_cast_fp16 = reshape(shape = var_1069, x = var_1068_cast_fp16)[name = string("input_183_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(37058880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37321088))))[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(37322176)))]; - tensor linear_47_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_183_cast_fp16)[name = string("linear_47_cast_fp16")]; - tensor var_1075_cast_fp16 = mul(x = var_1014_cast_fp16, y = linear_47_cast_fp16)[name = string("op_1075_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_181_cast_fp16, y = var_1075_cast_fp16)[name = string("input_187_cast_fp16")]; - tensor var_1077_axes_0 = const()[name = string("op_1077_axes_0"), val = tensor([1])]; - tensor var_1077_cast_fp16 = expand_dims(axes = var_1077_axes_0, x = var_1008_cast_fp16_5)[name = string("op_1077_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_69_to_fp16, x = input_187_cast_fp16)[name = string("x_49_cast_fp16")]; - tensor var_1080_axes_0 = const()[name = string("op_1080_axes_0"), val = tensor([1])]; - tensor var_1080_cast_fp16 = expand_dims(axes = var_1080_axes_0, x = var_1008_cast_fp16_4)[name = string("op_1080_cast_fp16")]; - fp16 var_1081_to_fp16 = const()[name = string("op_1081_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1082_cast_fp16 = add(x = var_1080_cast_fp16, y = var_1081_to_fp16)[name = string("op_1082_cast_fp16")]; - tensor var_1083_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1082_cast_fp16)[name = string("op_1083_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_1008_cast_fp16_3)[name = string("op_1084_cast_fp16")]; - tensor input_189_cast_fp16 = add(x = var_1083_cast_fp16, y = var_1084_cast_fp16)[name = string("input_189_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(37323264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38371904))))[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(38376064)))]; - tensor linear_48_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_189_cast_fp16)[name = string("linear_48_cast_fp16")]; - string input_193_mode_0 = const()[name = string("input_193_mode_0"), val = string("EXACT")]; - tensor input_193_cast_fp16 = gelu(mode = input_193_mode_0, x = linear_48_cast_fp16)[name = string("input_193_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(38380224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39428864))))[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(39429952)))]; - tensor linear_49_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_193_cast_fp16)[name = string("linear_49_cast_fp16")]; - tensor var_1097_cast_fp16 = mul(x = var_1077_cast_fp16, y = linear_49_cast_fp16)[name = string("op_1097_cast_fp16")]; - tensor input_201_cast_fp16 = add(x = input_187_cast_fp16, y = var_1097_cast_fp16)[name = string("input_201_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(39431040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41003968))))[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(41010176)))]; - tensor linear_50_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_39_cast_fp16)[name = string("linear_50_cast_fp16")]; - tensor var_1106_split_sizes_0 = const()[name = string("op_1106_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1106_axis_0 = const()[name = string("op_1106_axis_0"), val = int32(-1)]; - tensor var_1106_cast_fp16_0, tensor var_1106_cast_fp16_1, tensor var_1106_cast_fp16_2, tensor var_1106_cast_fp16_3, tensor var_1106_cast_fp16_4, tensor var_1106_cast_fp16_5 = split(axis = var_1106_axis_0, split_sizes = var_1106_split_sizes_0, x = linear_50_cast_fp16)[name = string("op_1106_cast_fp16")]; - tensor var_1112_axes_0 = const()[name = string("op_1112_axes_0"), val = tensor([1])]; - tensor var_1112_cast_fp16 = expand_dims(axes = var_1112_axes_0, x = var_1106_cast_fp16_2)[name = string("op_1112_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_69_to_fp16, x = input_201_cast_fp16)[name = string("x_51_cast_fp16")]; - tensor var_1115_axes_0 = const()[name = string("op_1115_axes_0"), val = tensor([1])]; - tensor var_1115_cast_fp16 = expand_dims(axes = var_1115_axes_0, x = var_1106_cast_fp16_1)[name = string("op_1115_cast_fp16")]; - fp16 var_1116_to_fp16 = const()[name = string("op_1116_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1117_cast_fp16 = add(x = var_1115_cast_fp16, y = var_1116_to_fp16)[name = string("op_1117_cast_fp16")]; - tensor var_1118_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1117_cast_fp16)[name = string("op_1118_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_1106_cast_fp16_0)[name = string("op_1119_cast_fp16")]; - tensor x_53_cast_fp16 = add(x = var_1118_cast_fp16, y = var_1119_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(41016384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41802880))))[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(41806016)))]; - tensor linear_51_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_51_cast_fp16")]; - tensor var_1131 = const()[name = string("op_1131"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_17_cast_fp16 = reshape(shape = var_1131, x = linear_51_cast_fp16)[name = string("qkv_17_cast_fp16")]; - tensor var_1133_split_sizes_0 = const()[name = string("op_1133_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1133_axis_0 = const()[name = string("op_1133_axis_0"), val = int32(2)]; - tensor var_1133_cast_fp16_0, tensor var_1133_cast_fp16_1, tensor var_1133_cast_fp16_2 = split(axis = var_1133_axis_0, split_sizes = var_1133_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1133_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_1133_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_1133_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_1133_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; - tensor var_1138_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1138_cast_fp16")]; - tensor var_1140_axes_0 = const()[name = string("op_1140_axes_0"), val = tensor([-1])]; - bool var_1140_keep_dims_0 = const()[name = string("op_1140_keep_dims_0"), val = bool(true)]; - tensor var_1140_cast_fp16 = reduce_mean(axes = var_1140_axes_0, keep_dims = var_1140_keep_dims_0, x = var_1138_cast_fp16)[name = string("op_1140_cast_fp16")]; - fp16 var_1141_to_fp16 = const()[name = string("op_1141_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1142_cast_fp16 = add(x = var_1140_cast_fp16, y = var_1141_to_fp16)[name = string("op_1142_cast_fp16")]; - fp32 var_1143_epsilon_0 = const()[name = string("op_1143_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1143_cast_fp16 = rsqrt(epsilon = var_1143_epsilon_0, x = var_1142_cast_fp16)[name = string("op_1143_cast_fp16")]; - tensor var_1144_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1143_cast_fp16)[name = string("op_1144_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(41809152)))]; - tensor var_1145_cast_fp16 = mul(x = var_1144_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1145_cast_fp16")]; - tensor var_1148_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1148_cast_fp16")]; - tensor var_1150_axes_0 = const()[name = string("op_1150_axes_0"), val = tensor([-1])]; - bool var_1150_keep_dims_0 = const()[name = string("op_1150_keep_dims_0"), val = bool(true)]; - tensor var_1150_cast_fp16 = reduce_mean(axes = var_1150_axes_0, keep_dims = var_1150_keep_dims_0, x = var_1148_cast_fp16)[name = string("op_1150_cast_fp16")]; - fp16 var_1151_to_fp16 = const()[name = string("op_1151_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1152_cast_fp16 = add(x = var_1150_cast_fp16, y = var_1151_to_fp16)[name = string("op_1152_cast_fp16")]; - fp32 var_1153_epsilon_0 = const()[name = string("op_1153_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1153_cast_fp16 = rsqrt(epsilon = var_1153_epsilon_0, x = var_1152_cast_fp16)[name = string("op_1153_cast_fp16")]; - tensor var_1154_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1153_cast_fp16)[name = string("op_1154_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(41809344)))]; - tensor var_1155_cast_fp16 = mul(x = var_1154_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1155_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_1155_cast_fp16)[name = string("transpose_123")]; - tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1145_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_1166_perm_0 = const()[name = string("op_1166_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1167 = const()[name = string("op_1167"), val = tensor([1, 1120, 512])]; - tensor var_1166_cast_fp16 = transpose(perm = var_1166_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; - tensor input_203_cast_fp16 = reshape(shape = var_1167, x = var_1166_cast_fp16)[name = string("input_203_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(41809536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42071744))))[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(42072832)))]; - tensor linear_52_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_203_cast_fp16)[name = string("linear_52_cast_fp16")]; - tensor var_1173_cast_fp16 = mul(x = var_1112_cast_fp16, y = linear_52_cast_fp16)[name = string("op_1173_cast_fp16")]; - tensor input_207_cast_fp16 = add(x = input_201_cast_fp16, y = var_1173_cast_fp16)[name = string("input_207_cast_fp16")]; - tensor var_1175_axes_0 = const()[name = string("op_1175_axes_0"), val = tensor([1])]; - tensor var_1175_cast_fp16 = expand_dims(axes = var_1175_axes_0, x = var_1106_cast_fp16_5)[name = string("op_1175_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_69_to_fp16, x = input_207_cast_fp16)[name = string("x_55_cast_fp16")]; - tensor var_1178_axes_0 = const()[name = string("op_1178_axes_0"), val = tensor([1])]; - tensor var_1178_cast_fp16 = expand_dims(axes = var_1178_axes_0, x = var_1106_cast_fp16_4)[name = string("op_1178_cast_fp16")]; - fp16 var_1179_to_fp16 = const()[name = string("op_1179_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1180_cast_fp16 = add(x = var_1178_cast_fp16, y = var_1179_to_fp16)[name = string("op_1180_cast_fp16")]; - tensor var_1181_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1180_cast_fp16)[name = string("op_1181_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_1106_cast_fp16_3)[name = string("op_1182_cast_fp16")]; - tensor input_209_cast_fp16 = add(x = var_1181_cast_fp16, y = var_1182_cast_fp16)[name = string("input_209_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(42073920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43122560))))[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(43126720)))]; - tensor linear_53_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_209_cast_fp16)[name = string("linear_53_cast_fp16")]; - string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("EXACT")]; - tensor input_213_cast_fp16 = gelu(mode = input_213_mode_0, x = linear_53_cast_fp16)[name = string("input_213_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(43130880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44179520))))[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(44180608)))]; - tensor linear_54_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_213_cast_fp16)[name = string("linear_54_cast_fp16")]; - tensor var_1195_cast_fp16 = mul(x = var_1175_cast_fp16, y = linear_54_cast_fp16)[name = string("op_1195_cast_fp16")]; - tensor input_221_cast_fp16 = add(x = input_207_cast_fp16, y = var_1195_cast_fp16)[name = string("input_221_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(44181696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45754624))))[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(45760832)))]; - tensor linear_55_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_39_cast_fp16)[name = string("linear_55_cast_fp16")]; - tensor var_1204_split_sizes_0 = const()[name = string("op_1204_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1204_axis_0 = const()[name = string("op_1204_axis_0"), val = int32(-1)]; - tensor var_1204_cast_fp16_0, tensor var_1204_cast_fp16_1, tensor var_1204_cast_fp16_2, tensor var_1204_cast_fp16_3, tensor var_1204_cast_fp16_4, tensor var_1204_cast_fp16_5 = split(axis = var_1204_axis_0, split_sizes = var_1204_split_sizes_0, x = linear_55_cast_fp16)[name = string("op_1204_cast_fp16")]; - tensor var_1210_axes_0 = const()[name = string("op_1210_axes_0"), val = tensor([1])]; - tensor var_1210_cast_fp16 = expand_dims(axes = var_1210_axes_0, x = var_1204_cast_fp16_2)[name = string("op_1210_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_69_to_fp16, x = input_221_cast_fp16)[name = string("x_57_cast_fp16")]; - tensor var_1213_axes_0 = const()[name = string("op_1213_axes_0"), val = tensor([1])]; - tensor var_1213_cast_fp16 = expand_dims(axes = var_1213_axes_0, x = var_1204_cast_fp16_1)[name = string("op_1213_cast_fp16")]; - fp16 var_1214_to_fp16 = const()[name = string("op_1214_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1215_cast_fp16 = add(x = var_1213_cast_fp16, y = var_1214_to_fp16)[name = string("op_1215_cast_fp16")]; - tensor var_1216_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1215_cast_fp16)[name = string("op_1216_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_1204_cast_fp16_0)[name = string("op_1217_cast_fp16")]; - tensor x_59_cast_fp16 = add(x = var_1216_cast_fp16, y = var_1217_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(45767040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46553536))))[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(46556672)))]; - tensor linear_56_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_56_cast_fp16")]; - tensor var_1229 = const()[name = string("op_1229"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_19_cast_fp16 = reshape(shape = var_1229, x = linear_56_cast_fp16)[name = string("qkv_19_cast_fp16")]; - tensor var_1231_split_sizes_0 = const()[name = string("op_1231_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1231_axis_0 = const()[name = string("op_1231_axis_0"), val = int32(2)]; - tensor var_1231_cast_fp16_0, tensor var_1231_cast_fp16_1, tensor var_1231_cast_fp16_2 = split(axis = var_1231_axis_0, split_sizes = var_1231_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1231_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_1231_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_1231_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_1231_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; - tensor var_1236_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1236_cast_fp16")]; - tensor var_1238_axes_0 = const()[name = string("op_1238_axes_0"), val = tensor([-1])]; - bool var_1238_keep_dims_0 = const()[name = string("op_1238_keep_dims_0"), val = bool(true)]; - tensor var_1238_cast_fp16 = reduce_mean(axes = var_1238_axes_0, keep_dims = var_1238_keep_dims_0, x = var_1236_cast_fp16)[name = string("op_1238_cast_fp16")]; - fp16 var_1239_to_fp16 = const()[name = string("op_1239_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1240_cast_fp16 = add(x = var_1238_cast_fp16, y = var_1239_to_fp16)[name = string("op_1240_cast_fp16")]; - fp32 var_1241_epsilon_0 = const()[name = string("op_1241_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1241_cast_fp16 = rsqrt(epsilon = var_1241_epsilon_0, x = var_1240_cast_fp16)[name = string("op_1241_cast_fp16")]; - tensor var_1242_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1241_cast_fp16)[name = string("op_1242_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(46559808)))]; - tensor var_1243_cast_fp16 = mul(x = var_1242_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1243_cast_fp16")]; - tensor var_1246_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1246_cast_fp16")]; - tensor var_1248_axes_0 = const()[name = string("op_1248_axes_0"), val = tensor([-1])]; - bool var_1248_keep_dims_0 = const()[name = string("op_1248_keep_dims_0"), val = bool(true)]; - tensor var_1248_cast_fp16 = reduce_mean(axes = var_1248_axes_0, keep_dims = var_1248_keep_dims_0, x = var_1246_cast_fp16)[name = string("op_1248_cast_fp16")]; - fp16 var_1249_to_fp16 = const()[name = string("op_1249_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1250_cast_fp16 = add(x = var_1248_cast_fp16, y = var_1249_to_fp16)[name = string("op_1250_cast_fp16")]; - fp32 var_1251_epsilon_0 = const()[name = string("op_1251_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1251_cast_fp16 = rsqrt(epsilon = var_1251_epsilon_0, x = var_1250_cast_fp16)[name = string("op_1251_cast_fp16")]; - tensor var_1252_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1251_cast_fp16)[name = string("op_1252_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(46560000)))]; - tensor var_1253_cast_fp16 = mul(x = var_1252_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1253_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_1253_cast_fp16)[name = string("transpose_119")]; - tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1243_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_1264_perm_0 = const()[name = string("op_1264_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1265 = const()[name = string("op_1265"), val = tensor([1, 1120, 512])]; - tensor var_1264_cast_fp16 = transpose(perm = var_1264_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; - tensor input_223_cast_fp16 = reshape(shape = var_1265, x = var_1264_cast_fp16)[name = string("input_223_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(46560192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46822400))))[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(46823488)))]; - tensor linear_57_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_223_cast_fp16)[name = string("linear_57_cast_fp16")]; - tensor var_1271_cast_fp16 = mul(x = var_1210_cast_fp16, y = linear_57_cast_fp16)[name = string("op_1271_cast_fp16")]; - tensor input_227_cast_fp16 = add(x = input_221_cast_fp16, y = var_1271_cast_fp16)[name = string("input_227_cast_fp16")]; - tensor var_1273_axes_0 = const()[name = string("op_1273_axes_0"), val = tensor([1])]; - tensor var_1273_cast_fp16 = expand_dims(axes = var_1273_axes_0, x = var_1204_cast_fp16_5)[name = string("op_1273_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_69_to_fp16, x = input_227_cast_fp16)[name = string("x_61_cast_fp16")]; - tensor var_1276_axes_0 = const()[name = string("op_1276_axes_0"), val = tensor([1])]; - tensor var_1276_cast_fp16 = expand_dims(axes = var_1276_axes_0, x = var_1204_cast_fp16_4)[name = string("op_1276_cast_fp16")]; - fp16 var_1277_to_fp16 = const()[name = string("op_1277_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1278_cast_fp16 = add(x = var_1276_cast_fp16, y = var_1277_to_fp16)[name = string("op_1278_cast_fp16")]; - tensor var_1279_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1278_cast_fp16)[name = string("op_1279_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_1204_cast_fp16_3)[name = string("op_1280_cast_fp16")]; - tensor input_229_cast_fp16 = add(x = var_1279_cast_fp16, y = var_1280_cast_fp16)[name = string("input_229_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(46824576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47873216))))[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(47877376)))]; - tensor linear_58_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_229_cast_fp16)[name = string("linear_58_cast_fp16")]; - string input_233_mode_0 = const()[name = string("input_233_mode_0"), val = string("EXACT")]; - tensor input_233_cast_fp16 = gelu(mode = input_233_mode_0, x = linear_58_cast_fp16)[name = string("input_233_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(47881536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48930176))))[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(48931264)))]; - tensor linear_59_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_233_cast_fp16)[name = string("linear_59_cast_fp16")]; - tensor var_1293_cast_fp16 = mul(x = var_1273_cast_fp16, y = linear_59_cast_fp16)[name = string("op_1293_cast_fp16")]; - tensor input_241_cast_fp16 = add(x = input_227_cast_fp16, y = var_1293_cast_fp16)[name = string("input_241_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(48932352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50505280))))[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(50511488)))]; - tensor linear_60_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_39_cast_fp16)[name = string("linear_60_cast_fp16")]; - tensor var_1302_split_sizes_0 = const()[name = string("op_1302_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1302_axis_0 = const()[name = string("op_1302_axis_0"), val = int32(-1)]; - tensor var_1302_cast_fp16_0, tensor var_1302_cast_fp16_1, tensor var_1302_cast_fp16_2, tensor var_1302_cast_fp16_3, tensor var_1302_cast_fp16_4, tensor var_1302_cast_fp16_5 = split(axis = var_1302_axis_0, split_sizes = var_1302_split_sizes_0, x = linear_60_cast_fp16)[name = string("op_1302_cast_fp16")]; - tensor var_1308_axes_0 = const()[name = string("op_1308_axes_0"), val = tensor([1])]; - tensor var_1308_cast_fp16 = expand_dims(axes = var_1308_axes_0, x = var_1302_cast_fp16_2)[name = string("op_1308_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_69_to_fp16, x = input_241_cast_fp16)[name = string("x_63_cast_fp16")]; - tensor var_1311_axes_0 = const()[name = string("op_1311_axes_0"), val = tensor([1])]; - tensor var_1311_cast_fp16 = expand_dims(axes = var_1311_axes_0, x = var_1302_cast_fp16_1)[name = string("op_1311_cast_fp16")]; - fp16 var_1312_to_fp16 = const()[name = string("op_1312_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1313_cast_fp16 = add(x = var_1311_cast_fp16, y = var_1312_to_fp16)[name = string("op_1313_cast_fp16")]; - tensor var_1314_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1313_cast_fp16)[name = string("op_1314_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_1302_cast_fp16_0)[name = string("op_1315_cast_fp16")]; - tensor x_65_cast_fp16 = add(x = var_1314_cast_fp16, y = var_1315_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(50517696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51304192))))[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(51307328)))]; - tensor linear_61_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_61_cast_fp16")]; - tensor var_1327 = const()[name = string("op_1327"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_21_cast_fp16 = reshape(shape = var_1327, x = linear_61_cast_fp16)[name = string("qkv_21_cast_fp16")]; - tensor var_1329_split_sizes_0 = const()[name = string("op_1329_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1329_axis_0 = const()[name = string("op_1329_axis_0"), val = int32(2)]; - tensor var_1329_cast_fp16_0, tensor var_1329_cast_fp16_1, tensor var_1329_cast_fp16_2 = split(axis = var_1329_axis_0, split_sizes = var_1329_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1329_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_1329_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_1329_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_1329_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; - tensor var_1334_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1334_cast_fp16")]; - tensor var_1336_axes_0 = const()[name = string("op_1336_axes_0"), val = tensor([-1])]; - bool var_1336_keep_dims_0 = const()[name = string("op_1336_keep_dims_0"), val = bool(true)]; - tensor var_1336_cast_fp16 = reduce_mean(axes = var_1336_axes_0, keep_dims = var_1336_keep_dims_0, x = var_1334_cast_fp16)[name = string("op_1336_cast_fp16")]; - fp16 var_1337_to_fp16 = const()[name = string("op_1337_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1338_cast_fp16 = add(x = var_1336_cast_fp16, y = var_1337_to_fp16)[name = string("op_1338_cast_fp16")]; - fp32 var_1339_epsilon_0 = const()[name = string("op_1339_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1339_cast_fp16 = rsqrt(epsilon = var_1339_epsilon_0, x = var_1338_cast_fp16)[name = string("op_1339_cast_fp16")]; - tensor var_1340_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1339_cast_fp16)[name = string("op_1340_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(51310464)))]; - tensor var_1341_cast_fp16 = mul(x = var_1340_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1341_cast_fp16")]; - tensor var_1344_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1344_cast_fp16")]; - tensor var_1346_axes_0 = const()[name = string("op_1346_axes_0"), val = tensor([-1])]; - bool var_1346_keep_dims_0 = const()[name = string("op_1346_keep_dims_0"), val = bool(true)]; - tensor var_1346_cast_fp16 = reduce_mean(axes = var_1346_axes_0, keep_dims = var_1346_keep_dims_0, x = var_1344_cast_fp16)[name = string("op_1346_cast_fp16")]; - fp16 var_1347_to_fp16 = const()[name = string("op_1347_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1348_cast_fp16 = add(x = var_1346_cast_fp16, y = var_1347_to_fp16)[name = string("op_1348_cast_fp16")]; - fp32 var_1349_epsilon_0 = const()[name = string("op_1349_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1349_cast_fp16 = rsqrt(epsilon = var_1349_epsilon_0, x = var_1348_cast_fp16)[name = string("op_1349_cast_fp16")]; - tensor var_1350_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1349_cast_fp16)[name = string("op_1350_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(51310656)))]; - tensor var_1351_cast_fp16 = mul(x = var_1350_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1351_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_1351_cast_fp16)[name = string("transpose_115")]; - tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1341_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_1362_perm_0 = const()[name = string("op_1362_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1363 = const()[name = string("op_1363"), val = tensor([1, 1120, 512])]; - tensor var_1362_cast_fp16 = transpose(perm = var_1362_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; - tensor input_243_cast_fp16 = reshape(shape = var_1363, x = var_1362_cast_fp16)[name = string("input_243_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(51310848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51573056))))[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(51574144)))]; - tensor linear_62_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_243_cast_fp16)[name = string("linear_62_cast_fp16")]; - tensor var_1369_cast_fp16 = mul(x = var_1308_cast_fp16, y = linear_62_cast_fp16)[name = string("op_1369_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_241_cast_fp16, y = var_1369_cast_fp16)[name = string("input_247_cast_fp16")]; - tensor var_1371_axes_0 = const()[name = string("op_1371_axes_0"), val = tensor([1])]; - tensor var_1371_cast_fp16 = expand_dims(axes = var_1371_axes_0, x = var_1302_cast_fp16_5)[name = string("op_1371_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_69_to_fp16, x = input_247_cast_fp16)[name = string("x_67_cast_fp16")]; - tensor var_1374_axes_0 = const()[name = string("op_1374_axes_0"), val = tensor([1])]; - tensor var_1374_cast_fp16 = expand_dims(axes = var_1374_axes_0, x = var_1302_cast_fp16_4)[name = string("op_1374_cast_fp16")]; - fp16 var_1375_to_fp16 = const()[name = string("op_1375_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1376_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1375_to_fp16)[name = string("op_1376_cast_fp16")]; - tensor var_1377_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1376_cast_fp16)[name = string("op_1377_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_1302_cast_fp16_3)[name = string("op_1378_cast_fp16")]; - tensor input_249_cast_fp16 = add(x = var_1377_cast_fp16, y = var_1378_cast_fp16)[name = string("input_249_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(51575232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52623872))))[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(52628032)))]; - tensor linear_63_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_249_cast_fp16)[name = string("linear_63_cast_fp16")]; - string input_253_mode_0 = const()[name = string("input_253_mode_0"), val = string("EXACT")]; - tensor input_253_cast_fp16 = gelu(mode = input_253_mode_0, x = linear_63_cast_fp16)[name = string("input_253_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(52632192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53680832))))[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(53681920)))]; - tensor linear_64_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_253_cast_fp16)[name = string("linear_64_cast_fp16")]; - tensor var_1391_cast_fp16 = mul(x = var_1371_cast_fp16, y = linear_64_cast_fp16)[name = string("op_1391_cast_fp16")]; - tensor input_261_cast_fp16 = add(x = input_247_cast_fp16, y = var_1391_cast_fp16)[name = string("input_261_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(53683008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55255936))))[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(55262144)))]; - tensor linear_65_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_39_cast_fp16)[name = string("linear_65_cast_fp16")]; - tensor var_1400_split_sizes_0 = const()[name = string("op_1400_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1400_axis_0 = const()[name = string("op_1400_axis_0"), val = int32(-1)]; - tensor var_1400_cast_fp16_0, tensor var_1400_cast_fp16_1, tensor var_1400_cast_fp16_2, tensor var_1400_cast_fp16_3, tensor var_1400_cast_fp16_4, tensor var_1400_cast_fp16_5 = split(axis = var_1400_axis_0, split_sizes = var_1400_split_sizes_0, x = linear_65_cast_fp16)[name = string("op_1400_cast_fp16")]; - tensor var_1406_axes_0 = const()[name = string("op_1406_axes_0"), val = tensor([1])]; - tensor var_1406_cast_fp16 = expand_dims(axes = var_1406_axes_0, x = var_1400_cast_fp16_2)[name = string("op_1406_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_69_to_fp16, x = input_261_cast_fp16)[name = string("x_69_cast_fp16")]; - tensor var_1409_axes_0 = const()[name = string("op_1409_axes_0"), val = tensor([1])]; - tensor var_1409_cast_fp16 = expand_dims(axes = var_1409_axes_0, x = var_1400_cast_fp16_1)[name = string("op_1409_cast_fp16")]; - fp16 var_1410_to_fp16 = const()[name = string("op_1410_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1411_cast_fp16 = add(x = var_1409_cast_fp16, y = var_1410_to_fp16)[name = string("op_1411_cast_fp16")]; - tensor var_1412_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1411_cast_fp16)[name = string("op_1412_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_1400_cast_fp16_0)[name = string("op_1413_cast_fp16")]; - tensor x_71_cast_fp16 = add(x = var_1412_cast_fp16, y = var_1413_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(55268352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56054848))))[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(56057984)))]; - tensor linear_66_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_66_cast_fp16")]; - tensor var_1425 = const()[name = string("op_1425"), val = tensor([1, 1120, 3, 8, 64])]; - tensor qkv_23_cast_fp16 = reshape(shape = var_1425, x = linear_66_cast_fp16)[name = string("qkv_23_cast_fp16")]; - tensor var_1427_split_sizes_0 = const()[name = string("op_1427_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1427_axis_0 = const()[name = string("op_1427_axis_0"), val = int32(2)]; - tensor var_1427_cast_fp16_0, tensor var_1427_cast_fp16_1, tensor var_1427_cast_fp16_2 = split(axis = var_1427_axis_0, split_sizes = var_1427_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1427_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_1427_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_1427_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_1427_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; - tensor var_1432_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1432_cast_fp16")]; - tensor var_1434_axes_0 = const()[name = string("op_1434_axes_0"), val = tensor([-1])]; - bool var_1434_keep_dims_0 = const()[name = string("op_1434_keep_dims_0"), val = bool(true)]; - tensor var_1434_cast_fp16 = reduce_mean(axes = var_1434_axes_0, keep_dims = var_1434_keep_dims_0, x = var_1432_cast_fp16)[name = string("op_1434_cast_fp16")]; - fp16 var_1435_to_fp16 = const()[name = string("op_1435_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1436_cast_fp16 = add(x = var_1434_cast_fp16, y = var_1435_to_fp16)[name = string("op_1436_cast_fp16")]; - fp32 var_1437_epsilon_0 = const()[name = string("op_1437_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1437_cast_fp16 = rsqrt(epsilon = var_1437_epsilon_0, x = var_1436_cast_fp16)[name = string("op_1437_cast_fp16")]; - tensor var_1438_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1437_cast_fp16)[name = string("op_1438_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(56061120)))]; - tensor var_1439_cast_fp16 = mul(x = var_1438_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1439_cast_fp16")]; - tensor var_1442_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1442_cast_fp16")]; - tensor var_1444_axes_0 = const()[name = string("op_1444_axes_0"), val = tensor([-1])]; - bool var_1444_keep_dims_0 = const()[name = string("op_1444_keep_dims_0"), val = bool(true)]; - tensor var_1444_cast_fp16 = reduce_mean(axes = var_1444_axes_0, keep_dims = var_1444_keep_dims_0, x = var_1442_cast_fp16)[name = string("op_1444_cast_fp16")]; - fp16 var_1445_to_fp16 = const()[name = string("op_1445_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1446_cast_fp16 = add(x = var_1444_cast_fp16, y = var_1445_to_fp16)[name = string("op_1446_cast_fp16")]; - fp32 var_1447_epsilon_0 = const()[name = string("op_1447_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1447_cast_fp16 = rsqrt(epsilon = var_1447_epsilon_0, x = var_1446_cast_fp16)[name = string("op_1447_cast_fp16")]; - tensor var_1448_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1447_cast_fp16)[name = string("op_1448_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(56061312)))]; - tensor var_1449_cast_fp16 = mul(x = var_1448_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1449_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_1449_cast_fp16)[name = string("transpose_111")]; - tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1439_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_1460_perm_0 = const()[name = string("op_1460_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1461 = const()[name = string("op_1461"), val = tensor([1, 1120, 512])]; - tensor var_1460_cast_fp16 = transpose(perm = var_1460_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; - tensor input_263_cast_fp16 = reshape(shape = var_1461, x = var_1460_cast_fp16)[name = string("input_263_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(56061504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56323712))))[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(56324800)))]; - tensor linear_67_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_263_cast_fp16)[name = string("linear_67_cast_fp16")]; - tensor var_1467_cast_fp16 = mul(x = var_1406_cast_fp16, y = linear_67_cast_fp16)[name = string("op_1467_cast_fp16")]; - tensor input_267_cast_fp16 = add(x = input_261_cast_fp16, y = var_1467_cast_fp16)[name = string("input_267_cast_fp16")]; - tensor var_1469_axes_0 = const()[name = string("op_1469_axes_0"), val = tensor([1])]; - tensor var_1469_cast_fp16 = expand_dims(axes = var_1469_axes_0, x = var_1400_cast_fp16_5)[name = string("op_1469_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_69_to_fp16, x = input_267_cast_fp16)[name = string("x_73_cast_fp16")]; - tensor var_1472_axes_0 = const()[name = string("op_1472_axes_0"), val = tensor([1])]; - tensor var_1472_cast_fp16 = expand_dims(axes = var_1472_axes_0, x = var_1400_cast_fp16_4)[name = string("op_1472_cast_fp16")]; - fp16 var_1473_to_fp16 = const()[name = string("op_1473_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1474_cast_fp16 = add(x = var_1472_cast_fp16, y = var_1473_to_fp16)[name = string("op_1474_cast_fp16")]; - tensor var_1475_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1474_cast_fp16)[name = string("op_1475_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_1400_cast_fp16_3)[name = string("op_1476_cast_fp16")]; - tensor input_269_cast_fp16 = add(x = var_1475_cast_fp16, y = var_1476_cast_fp16)[name = string("input_269_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(56325888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57374528))))[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(57378688)))]; - tensor linear_68_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_269_cast_fp16)[name = string("linear_68_cast_fp16")]; - string input_273_mode_0 = const()[name = string("input_273_mode_0"), val = string("EXACT")]; - tensor input_273_cast_fp16 = gelu(mode = input_273_mode_0, x = linear_68_cast_fp16)[name = string("input_273_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(57382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58431488))))[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(58432576)))]; - tensor linear_69_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_273_cast_fp16)[name = string("linear_69_cast_fp16")]; - tensor var_1489_cast_fp16 = mul(x = var_1469_cast_fp16, y = linear_69_cast_fp16)[name = string("op_1489_cast_fp16")]; - tensor input_281_cast_fp16 = add(x = input_267_cast_fp16, y = var_1489_cast_fp16)[name = string("input_281_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(58433664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58958016))))[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(58960128)))]; - tensor linear_70_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_39_cast_fp16)[name = string("linear_70_cast_fp16")]; - tensor var_1495_split_sizes_0 = const()[name = string("op_1495_split_sizes_0"), val = tensor([512, 512])]; - int32 var_1495_axis_0 = const()[name = string("op_1495_axis_0"), val = int32(-1)]; - tensor var_1495_cast_fp16_0, tensor var_1495_cast_fp16_1 = split(axis = var_1495_axis_0, split_sizes = var_1495_split_sizes_0, x = linear_70_cast_fp16)[name = string("op_1495_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_69_to_fp16, x = input_281_cast_fp16)[name = string("x_cast_fp16")]; - tensor var_1499_axes_0 = const()[name = string("op_1499_axes_0"), val = tensor([1])]; - tensor var_1499_cast_fp16 = expand_dims(axes = var_1499_axes_0, x = var_1495_cast_fp16_1)[name = string("op_1499_cast_fp16")]; - fp16 var_1500_to_fp16 = const()[name = string("op_1500_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1501_cast_fp16 = add(x = var_1499_cast_fp16, y = var_1500_to_fp16)[name = string("op_1501_cast_fp16")]; - tensor var_1502_cast_fp16 = mul(x = x_cast_fp16, y = var_1501_cast_fp16)[name = string("op_1502_cast_fp16")]; - tensor var_1503_axes_0 = const()[name = string("op_1503_axes_0"), val = tensor([1])]; - tensor var_1503_cast_fp16 = expand_dims(axes = var_1503_axes_0, x = var_1495_cast_fp16_0)[name = string("op_1503_cast_fp16")]; - tensor encoded_cast_fp16 = add(x = var_1502_cast_fp16, y = var_1503_cast_fp16)[name = string("encoded_cast_fp16")]; - tensor input_283_begin_0 = const()[name = string("input_283_begin_0"), val = tensor([0, 96, 0])]; - tensor input_283_end_0 = const()[name = string("input_283_end_0"), val = tensor([1, 1120, 512])]; - tensor input_283_end_mask_0 = const()[name = string("input_283_end_mask_0"), val = tensor([true, true, true])]; - tensor input_283_cast_fp16 = slice_by_index(begin = input_283_begin_0, end = input_283_end_0, end_mask = input_283_end_mask_0, x = encoded_cast_fp16)[name = string("input_283_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(58962240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59224448))))[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(59225536)))]; - tensor linear_71_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_71_cast_fp16")]; - string input_287_mode_0 = const()[name = string("input_287_mode_0"), val = string("EXACT")]; - tensor input_287_cast_fp16 = gelu(mode = input_287_mode_0, x = linear_71_cast_fp16)[name = string("input_287_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(59226624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59238976))))[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(59239104)))]; - tensor linear_72_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_72_cast_fp16")]; - tensor var_1517_perm_0 = const()[name = string("op_1517_perm_0"), val = tensor([0, 2, 1])]; - string var_1517_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1517_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; - tensor var_1517_cast_fp16 = transpose(perm = var_1517_perm_0, x = linear_72_cast_fp16)[name = string("transpose_108")]; - tensor velocity = cast(dtype = var_1517_cast_fp16_to_fp32_dtype_0, x = var_1517_cast_fp16)[name = string("cast_1")]; - } -> (velocity); - func vector_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 prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { - int32 var_33 = const()[name = string("op_33"), val = int32(0)]; - tensor present = not_equal(x = delivery_present, y = var_33)[name = string("present")]; - int32 var_35 = const()[name = string("op_35"), val = int32(0)]; - tensor latent_mask_1 = not_equal(x = latent_mask, y = var_35)[name = string("latent_mask")]; - int32 var_37 = const()[name = string("op_37"), val = int32(0)]; - tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_37)[name = string("prefix_mask_1")]; - int32 var_58 = const()[name = string("op_58"), val = int32(-1)]; - int32 var_73 = const()[name = string("op_73"), val = int32(1)]; - int32 var_87 = const()[name = string("op_87"), val = int32(7)]; - tensor var_88 = mul(x = voice_id, y = var_87)[name = string("op_88")]; - tensor index = add(x = var_88, 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(35968))))[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_20")]; - tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_19")]; - 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(70)]; - 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_18")]; - tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_17")]; - 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(70)]; - 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(36224)))]; - 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_16")]; - 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(40768)))]; - 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(45312)))]; - 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(49856)))]; - 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(50112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62464))))[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(63552)))]; - tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_15")]; - 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_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_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109248))))[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_14")]; - tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; - tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; - int32 var_150_axis_0 = const()[name = string("op_150_axis_0"), val = int32(0)]; - int32 var_150_batch_dims_0 = const()[name = string("op_150_batch_dims_0"), val = int32(0)]; - bool var_150_validate_indices_0 = const()[name = string("op_150_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(109504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154624))))[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_13")]; - tensor var_150_cast_fp16_cast_uint16 = gather(axis = var_150_axis_0, batch_dims = var_150_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_150_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_150_cast_fp16_cast_uint16")]; - int32 var_155_axis_0 = const()[name = string("op_155_axis_0"), val = int32(0)]; - int32 var_155_batch_dims_0 = const()[name = string("op_155_batch_dims_0"), val = int32(0)]; - bool var_155_validate_indices_0 = const()[name = string("op_155_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(154880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187712))))[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_12")]; - tensor var_155_cast_fp16_cast_uint16 = gather(axis = var_155_axis_0, batch_dims = var_155_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_155_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_155_cast_fp16_cast_uint16")]; - tensor hidden_7_cast_fp16 = add(x = var_150_cast_fp16_cast_uint16, y = var_155_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_10")]; - 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_11")]; - 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(187904)))]; - 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(190016)))]; - 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_166_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_166_cast_fp16")]; - tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_166_cast_fp16)[name = string("hidden_11_cast_fp16")]; - int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(0)]; - int32 var_170_batch_dims_0 = const()[name = string("op_170_batch_dims_0"), val = int32(0)]; - bool var_170_validate_indices_0 = const()[name = string("op_170_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(191104)))]; - 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_9")]; - tensor var_170_cast_fp16_cast_uint16 = gather(axis = var_170_axis_0, batch_dims = var_170_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_170_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_170_cast_fp16_cast_uint16")]; - tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_170_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; - tensor op_174_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196288))), scale = tensor([[[0x1.21p-5]]]))[name = string("op_174_to_fp16_quantized")]; - tensor hidden_17_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_174_to_fp16_quantized)[name = string("hidden_17_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(261888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267072))))[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(267200)))]; - 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_186_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_186_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_58, interleave = input_15_interleave_0, values = (var_186_cast_fp16, present_to_fp16))[name = string("input_15_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(274432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279616))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; - 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(280704)))]; - tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; - tensor var_204_axes_0 = const()[name = string("op_204_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_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = time_to_fp16)[name = string("op_204_cast_fp16")]; - tensor var_205_to_fp16 = const()[name = string("op_205_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281792)))]; - tensor phases_1_cast_fp16 = mul(x = var_204_cast_fp16, y = var_205_to_fp16)[name = string("phases_1_cast_fp16")]; - fp16 var_207_to_fp16 = const()[name = string("op_207_to_fp16"), val = fp16(0x1.92p+2)]; - tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_207_to_fp16)[name = string("phases_cast_fp16")]; - tensor var_209_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_209_cast_fp16")]; - tensor var_210_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_210_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_58, interleave = input_21_interleave_0, values = (var_209_cast_fp16, var_210_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(282368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(544576))))[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(545664)))]; - 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(546752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808960))))[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(810048)))]; - 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_220_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_220_cast_fp16")]; - tensor var_221_cast_fp16 = add(x = var_220_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_221_cast_fp16")]; - tensor var_222_cast_fp16 = add(x = var_221_cast_fp16, y = linear_2_cast_fp16)[name = string("op_222_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(811136)))]; - tensor cond_1_cast_fp16 = add(x = var_222_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; - fp16 var_61_to_fp16 = const()[name = string("op_61_to_fp16"), val = fp16(0x0p+0)]; - fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x1p+0)]; - tensor clip_0_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; - tensor clip_1_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; - tensor var_236_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_236_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(812224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(828672))))[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(829760)))]; - 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_236_cast_fp16)[name = string("linear_5_cast_fp16")]; - tensor var_242_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_242_cast_fp16")]; - tensor var_243_axes_0 = const()[name = string("op_243_axes_0"), val = tensor([-1])]; - tensor var_243_cast_fp16 = expand_dims(axes = var_243_axes_0, x = clip_1_cast_fp16)[name = string("op_243_cast_fp16")]; - tensor var_244_cast_fp16 = mul(x = var_242_cast_fp16, y = var_243_cast_fp16)[name = string("op_244_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(830848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(847296))))[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_244_cast_fp16)[name = string("linear_6_cast_fp16")]; - tensor var_250_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_250_cast_fp16")]; - tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_250_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_260_axes_0 = const()[name = string("op_260_axes_0"), val = tensor([-1])]; - tensor var_260_cast_fp16 = expand_dims(axes = var_260_axes_0, x = mask_7_cast_fp16)[name = string("op_260_cast_fp16")]; - tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_260_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(848384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110592))))[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(1111680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1373888))))[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_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_37_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_37_cast_fp16")]; - tensor input_33_perm_0 = const()[name = string("input_33_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_33_cast_fp16 = transpose(perm = input_33_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; - tensor linear_9_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_9_cast_fp16")]; - tensor op_277_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1374976))), scale = tensor([[[0x1.278p-5]]]))[name = string("op_277_to_fp16_quantized")]; - tensor context_3_cast_fp16 = add(x = linear_9_cast_fp16, y = op_277_to_fp16_quantized)[name = string("context_3_cast_fp16")]; - tensor var_280_to_fp16 = const()[name = string("op_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1424192)))]; - tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_280_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_73, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_17_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_73, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; - fp16 var_288_to_fp16 = const()[name = string("op_288_to_fp16"), val = fp16(0x1p-3)]; - tensor input_41_cast_fp16 = mul(x = x_1_cast_fp16, y = var_288_to_fp16)[name = string("input_41_cast_fp16")]; - tensor input_39_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("input_39_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(1425280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2998208))))[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(3004416)))]; - tensor linear_10_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_39_cast_fp16)[name = string("linear_10_cast_fp16")]; - tensor var_322_split_sizes_0 = const()[name = string("op_322_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_322_axis_0 = const()[name = string("op_322_axis_0"), val = int32(-1)]; - tensor var_322_cast_fp16_0, tensor var_322_cast_fp16_1, tensor var_322_cast_fp16_2, tensor var_322_cast_fp16_3, tensor var_322_cast_fp16_4, tensor var_322_cast_fp16_5 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = linear_10_cast_fp16)[name = string("op_322_cast_fp16")]; - tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; - tensor var_328_cast_fp16 = expand_dims(axes = var_328_axes_0, x = var_322_cast_fp16_2)[name = string("op_328_cast_fp16")]; - tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; - fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x1.8p-23)]; - tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_69_to_fp16, x = input_41_cast_fp16)[name = string("x_3_cast_fp16")]; - tensor var_331_axes_0 = const()[name = string("op_331_axes_0"), val = tensor([1])]; - tensor var_331_cast_fp16 = expand_dims(axes = var_331_axes_0, x = var_322_cast_fp16_1)[name = string("op_331_cast_fp16")]; - fp16 var_332_to_fp16 = const()[name = string("op_332_to_fp16"), val = fp16(0x1p+0)]; - tensor var_333_cast_fp16 = add(x = var_331_cast_fp16, y = var_332_to_fp16)[name = string("op_333_cast_fp16")]; - tensor var_334_cast_fp16 = mul(x = x_3_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_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_322_cast_fp16_0)[name = string("op_335_cast_fp16")]; - tensor x_5_cast_fp16 = add(x = var_334_cast_fp16, y = var_335_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(3010624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3797120))))[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(3800256)))]; - tensor linear_11_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_11_cast_fp16")]; - tensor var_347 = const()[name = string("op_347"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_1_cast_fp16 = reshape(shape = var_347, x = linear_11_cast_fp16)[name = string("qkv_1_cast_fp16")]; - tensor var_349_split_sizes_0 = const()[name = string("op_349_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_349_axis_0 = const()[name = string("op_349_axis_0"), val = int32(2)]; - tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1, tensor var_349_cast_fp16_2 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_349_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_349_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_349_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_349_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; - tensor var_354_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_354_cast_fp16")]; - tensor var_356_axes_0 = const()[name = string("op_356_axes_0"), val = tensor([-1])]; - bool var_356_keep_dims_0 = const()[name = string("op_356_keep_dims_0"), val = bool(true)]; - tensor var_356_cast_fp16 = reduce_mean(axes = var_356_axes_0, keep_dims = var_356_keep_dims_0, x = var_354_cast_fp16)[name = string("op_356_cast_fp16")]; - fp16 var_357_to_fp16 = const()[name = string("op_357_to_fp16"), val = fp16(0x1p-23)]; - tensor var_358_cast_fp16 = add(x = var_356_cast_fp16, y = var_357_to_fp16)[name = string("op_358_cast_fp16")]; - fp32 var_359_epsilon_0 = const()[name = string("op_359_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_359_cast_fp16 = rsqrt(epsilon = var_359_epsilon_0, x = var_358_cast_fp16)[name = string("op_359_cast_fp16")]; - tensor var_360_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_359_cast_fp16)[name = string("op_360_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(3803392)))]; - tensor var_361_cast_fp16 = mul(x = var_360_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_361_cast_fp16")]; - tensor var_364_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_364_cast_fp16")]; - tensor var_366_axes_0 = const()[name = string("op_366_axes_0"), val = tensor([-1])]; - bool var_366_keep_dims_0 = const()[name = string("op_366_keep_dims_0"), val = bool(true)]; - tensor var_366_cast_fp16 = reduce_mean(axes = var_366_axes_0, keep_dims = var_366_keep_dims_0, x = var_364_cast_fp16)[name = string("op_366_cast_fp16")]; - fp16 var_367_to_fp16 = const()[name = string("op_367_to_fp16"), val = fp16(0x1p-23)]; - tensor var_368_cast_fp16 = add(x = var_366_cast_fp16, y = var_367_to_fp16)[name = string("op_368_cast_fp16")]; - fp32 var_369_epsilon_0 = const()[name = string("op_369_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_369_cast_fp16 = rsqrt(epsilon = var_369_epsilon_0, x = var_368_cast_fp16)[name = string("op_369_cast_fp16")]; - tensor var_370_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_369_cast_fp16)[name = string("op_370_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(3803584)))]; - tensor var_371_cast_fp16 = mul(x = var_370_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_371_cast_fp16")]; - tensor var_376 = const()[name = string("op_376"), val = tensor([1, 1, 1, 224])]; - tensor var_377 = reshape(shape = var_376, x = valid_mask)[name = string("op_377")]; - tensor var_379 = logical_not(x = var_377)[name = string("op_379")]; - fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(-inf)]; - tensor var_375_to_fp16 = const()[name = string("op_375_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3803776)))]; - tensor attn_mask_1_cast_fp16 = select(a = var_68_to_fp16, b = var_375_to_fp16, cond = var_379)[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_371_cast_fp16)[name = string("transpose_155")]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_361_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_382_perm_0 = const()[name = string("op_382_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_383 = const()[name = string("op_383"), val = tensor([1, 224, 512])]; - tensor var_382_cast_fp16 = transpose(perm = var_382_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; - tensor input_43_cast_fp16 = reshape(shape = var_383, x = var_382_cast_fp16)[name = string("input_43_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(3804288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4066496))))[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(4067584)))]; - tensor linear_12_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_43_cast_fp16)[name = string("linear_12_cast_fp16")]; - tensor var_389_cast_fp16 = mul(x = var_328_cast_fp16, y = linear_12_cast_fp16)[name = string("op_389_cast_fp16")]; - tensor input_47_cast_fp16 = add(x = input_41_cast_fp16, y = var_389_cast_fp16)[name = string("input_47_cast_fp16")]; - tensor var_391_axes_0 = const()[name = string("op_391_axes_0"), val = tensor([1])]; - tensor var_391_cast_fp16 = expand_dims(axes = var_391_axes_0, x = var_322_cast_fp16_5)[name = string("op_391_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_69_to_fp16, x = input_47_cast_fp16)[name = string("x_7_cast_fp16")]; - tensor var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor([1])]; - tensor var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_322_cast_fp16_4)[name = string("op_394_cast_fp16")]; - fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p+0)]; - tensor var_396_cast_fp16 = add(x = var_394_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; - tensor var_397_cast_fp16 = mul(x = x_7_cast_fp16, y = var_396_cast_fp16)[name = string("op_397_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_322_cast_fp16_3)[name = string("op_398_cast_fp16")]; - tensor input_49_cast_fp16 = add(x = var_397_cast_fp16, y = var_398_cast_fp16)[name = string("input_49_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(4068672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5117312))))[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(5121472)))]; - tensor linear_13_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_49_cast_fp16)[name = string("linear_13_cast_fp16")]; - string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; - tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_13_cast_fp16)[name = string("input_53_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(5125632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6174272))))[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(6175360)))]; - tensor linear_14_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_53_cast_fp16)[name = string("linear_14_cast_fp16")]; - tensor var_411_cast_fp16 = mul(x = var_391_cast_fp16, y = linear_14_cast_fp16)[name = string("op_411_cast_fp16")]; - tensor input_61_cast_fp16 = add(x = input_47_cast_fp16, y = var_411_cast_fp16)[name = string("input_61_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(6176448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7749376))))[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(7755584)))]; - tensor linear_15_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_39_cast_fp16)[name = string("linear_15_cast_fp16")]; - tensor var_420_split_sizes_0 = const()[name = string("op_420_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_420_axis_0 = const()[name = string("op_420_axis_0"), val = int32(-1)]; - tensor var_420_cast_fp16_0, tensor var_420_cast_fp16_1, tensor var_420_cast_fp16_2, tensor var_420_cast_fp16_3, tensor var_420_cast_fp16_4, tensor var_420_cast_fp16_5 = split(axis = var_420_axis_0, split_sizes = var_420_split_sizes_0, x = linear_15_cast_fp16)[name = string("op_420_cast_fp16")]; - tensor var_426_axes_0 = const()[name = string("op_426_axes_0"), val = tensor([1])]; - tensor var_426_cast_fp16 = expand_dims(axes = var_426_axes_0, x = var_420_cast_fp16_2)[name = string("op_426_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_69_to_fp16, x = input_61_cast_fp16)[name = string("x_9_cast_fp16")]; - tensor var_429_axes_0 = const()[name = string("op_429_axes_0"), val = tensor([1])]; - tensor var_429_cast_fp16 = expand_dims(axes = var_429_axes_0, x = var_420_cast_fp16_1)[name = string("op_429_cast_fp16")]; - fp16 var_430_to_fp16 = const()[name = string("op_430_to_fp16"), val = fp16(0x1p+0)]; - tensor var_431_cast_fp16 = add(x = var_429_cast_fp16, y = var_430_to_fp16)[name = string("op_431_cast_fp16")]; - tensor var_432_cast_fp16 = mul(x = x_9_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_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_420_cast_fp16_0)[name = string("op_433_cast_fp16")]; - tensor x_11_cast_fp16 = add(x = var_432_cast_fp16, y = var_433_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(7761792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548288))))[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(8551424)))]; - tensor linear_16_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_16_cast_fp16")]; - tensor var_445 = const()[name = string("op_445"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_3_cast_fp16 = reshape(shape = var_445, x = linear_16_cast_fp16)[name = string("qkv_3_cast_fp16")]; - tensor var_447_split_sizes_0 = const()[name = string("op_447_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_447_axis_0 = const()[name = string("op_447_axis_0"), val = int32(2)]; - tensor var_447_cast_fp16_0, tensor var_447_cast_fp16_1, tensor var_447_cast_fp16_2 = split(axis = var_447_axis_0, split_sizes = var_447_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_447_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_447_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_447_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_447_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; - tensor var_452_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_452_cast_fp16")]; - tensor var_454_axes_0 = const()[name = string("op_454_axes_0"), val = tensor([-1])]; - bool var_454_keep_dims_0 = const()[name = string("op_454_keep_dims_0"), val = bool(true)]; - tensor var_454_cast_fp16 = reduce_mean(axes = var_454_axes_0, keep_dims = var_454_keep_dims_0, x = var_452_cast_fp16)[name = string("op_454_cast_fp16")]; - fp16 var_455_to_fp16 = const()[name = string("op_455_to_fp16"), val = fp16(0x1p-23)]; - tensor var_456_cast_fp16 = add(x = var_454_cast_fp16, y = var_455_to_fp16)[name = string("op_456_cast_fp16")]; - fp32 var_457_epsilon_0 = const()[name = string("op_457_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_457_cast_fp16 = rsqrt(epsilon = var_457_epsilon_0, x = var_456_cast_fp16)[name = string("op_457_cast_fp16")]; - tensor var_458_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_457_cast_fp16)[name = string("op_458_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(8554560)))]; - tensor var_459_cast_fp16 = mul(x = var_458_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_459_cast_fp16")]; - tensor var_462_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_462_cast_fp16")]; - tensor var_464_axes_0 = const()[name = string("op_464_axes_0"), val = tensor([-1])]; - bool var_464_keep_dims_0 = const()[name = string("op_464_keep_dims_0"), val = bool(true)]; - tensor var_464_cast_fp16 = reduce_mean(axes = var_464_axes_0, keep_dims = var_464_keep_dims_0, x = var_462_cast_fp16)[name = string("op_464_cast_fp16")]; - fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1p-23)]; - tensor var_466_cast_fp16 = add(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("op_466_cast_fp16")]; - fp32 var_467_epsilon_0 = const()[name = string("op_467_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_467_cast_fp16 = rsqrt(epsilon = var_467_epsilon_0, x = var_466_cast_fp16)[name = string("op_467_cast_fp16")]; - tensor var_468_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_467_cast_fp16)[name = string("op_468_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(8554752)))]; - tensor var_469_cast_fp16 = mul(x = var_468_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_469_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_469_cast_fp16)[name = string("transpose_151")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_459_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_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, 224, 512])]; - tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; - tensor input_63_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("input_63_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(8554944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8817152))))[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(8818240)))]; - tensor linear_17_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_63_cast_fp16)[name = string("linear_17_cast_fp16")]; - tensor var_487_cast_fp16 = mul(x = var_426_cast_fp16, y = linear_17_cast_fp16)[name = string("op_487_cast_fp16")]; - tensor input_67_cast_fp16 = add(x = input_61_cast_fp16, y = var_487_cast_fp16)[name = string("input_67_cast_fp16")]; - tensor var_489_axes_0 = const()[name = string("op_489_axes_0"), val = tensor([1])]; - tensor var_489_cast_fp16 = expand_dims(axes = var_489_axes_0, x = var_420_cast_fp16_5)[name = string("op_489_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_69_to_fp16, x = input_67_cast_fp16)[name = string("x_13_cast_fp16")]; - tensor var_492_axes_0 = const()[name = string("op_492_axes_0"), val = tensor([1])]; - tensor var_492_cast_fp16 = expand_dims(axes = var_492_axes_0, x = var_420_cast_fp16_4)[name = string("op_492_cast_fp16")]; - fp16 var_493_to_fp16 = const()[name = string("op_493_to_fp16"), val = fp16(0x1p+0)]; - tensor var_494_cast_fp16 = add(x = var_492_cast_fp16, y = var_493_to_fp16)[name = string("op_494_cast_fp16")]; - tensor var_495_cast_fp16 = mul(x = x_13_cast_fp16, y = var_494_cast_fp16)[name = string("op_495_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_420_cast_fp16_3)[name = string("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = var_495_cast_fp16, y = var_496_cast_fp16)[name = string("input_69_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(8819328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867968))))[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(9872128)))]; - tensor linear_18_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_69_cast_fp16)[name = string("linear_18_cast_fp16")]; - string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")]; - tensor input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = linear_18_cast_fp16)[name = string("input_73_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(9876288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10924928))))[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(10926016)))]; - tensor linear_19_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_73_cast_fp16)[name = string("linear_19_cast_fp16")]; - tensor var_509_cast_fp16 = mul(x = var_489_cast_fp16, y = linear_19_cast_fp16)[name = string("op_509_cast_fp16")]; - tensor input_81_cast_fp16 = add(x = input_67_cast_fp16, y = var_509_cast_fp16)[name = string("input_81_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(10927104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12500032))))[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(12506240)))]; - tensor linear_20_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_39_cast_fp16)[name = string("linear_20_cast_fp16")]; - tensor var_518_split_sizes_0 = const()[name = string("op_518_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_518_axis_0 = const()[name = string("op_518_axis_0"), val = int32(-1)]; - tensor var_518_cast_fp16_0, tensor var_518_cast_fp16_1, tensor var_518_cast_fp16_2, tensor var_518_cast_fp16_3, tensor var_518_cast_fp16_4, tensor var_518_cast_fp16_5 = split(axis = var_518_axis_0, split_sizes = var_518_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_518_cast_fp16")]; - tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; - tensor var_524_cast_fp16 = expand_dims(axes = var_524_axes_0, x = var_518_cast_fp16_2)[name = string("op_524_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_69_to_fp16, x = input_81_cast_fp16)[name = string("x_15_cast_fp16")]; - tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([1])]; - tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = var_518_cast_fp16_1)[name = string("op_527_cast_fp16")]; - fp16 var_528_to_fp16 = const()[name = string("op_528_to_fp16"), val = fp16(0x1p+0)]; - tensor var_529_cast_fp16 = add(x = var_527_cast_fp16, y = var_528_to_fp16)[name = string("op_529_cast_fp16")]; - tensor var_530_cast_fp16 = mul(x = x_15_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_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_518_cast_fp16_0)[name = string("op_531_cast_fp16")]; - tensor x_17_cast_fp16 = add(x = var_530_cast_fp16, y = var_531_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(12512448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13298944))))[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(13302080)))]; - tensor linear_21_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_21_cast_fp16")]; - tensor var_543 = const()[name = string("op_543"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_5_cast_fp16 = reshape(shape = var_543, x = linear_21_cast_fp16)[name = string("qkv_5_cast_fp16")]; - tensor var_545_split_sizes_0 = const()[name = string("op_545_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_545_axis_0 = const()[name = string("op_545_axis_0"), val = int32(2)]; - tensor var_545_cast_fp16_0, tensor var_545_cast_fp16_1, tensor var_545_cast_fp16_2 = split(axis = var_545_axis_0, split_sizes = var_545_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_545_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_545_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_545_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_545_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; - tensor var_550_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_550_cast_fp16")]; - tensor var_552_axes_0 = const()[name = string("op_552_axes_0"), val = tensor([-1])]; - bool var_552_keep_dims_0 = const()[name = string("op_552_keep_dims_0"), val = bool(true)]; - tensor var_552_cast_fp16 = reduce_mean(axes = var_552_axes_0, keep_dims = var_552_keep_dims_0, x = var_550_cast_fp16)[name = string("op_552_cast_fp16")]; - fp16 var_553_to_fp16 = const()[name = string("op_553_to_fp16"), val = fp16(0x1p-23)]; - tensor var_554_cast_fp16 = add(x = var_552_cast_fp16, y = var_553_to_fp16)[name = string("op_554_cast_fp16")]; - fp32 var_555_epsilon_0 = const()[name = string("op_555_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_555_cast_fp16 = rsqrt(epsilon = var_555_epsilon_0, x = var_554_cast_fp16)[name = string("op_555_cast_fp16")]; - tensor var_556_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_555_cast_fp16)[name = string("op_556_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(13305216)))]; - tensor var_557_cast_fp16 = mul(x = var_556_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_557_cast_fp16")]; - tensor var_560_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_560_cast_fp16")]; - tensor var_562_axes_0 = const()[name = string("op_562_axes_0"), val = tensor([-1])]; - bool var_562_keep_dims_0 = const()[name = string("op_562_keep_dims_0"), val = bool(true)]; - tensor var_562_cast_fp16 = reduce_mean(axes = var_562_axes_0, keep_dims = var_562_keep_dims_0, x = var_560_cast_fp16)[name = string("op_562_cast_fp16")]; - fp16 var_563_to_fp16 = const()[name = string("op_563_to_fp16"), val = fp16(0x1p-23)]; - tensor var_564_cast_fp16 = add(x = var_562_cast_fp16, y = var_563_to_fp16)[name = string("op_564_cast_fp16")]; - fp32 var_565_epsilon_0 = const()[name = string("op_565_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_565_cast_fp16 = rsqrt(epsilon = var_565_epsilon_0, x = var_564_cast_fp16)[name = string("op_565_cast_fp16")]; - tensor var_566_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_565_cast_fp16)[name = string("op_566_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(13305408)))]; - tensor var_567_cast_fp16 = mul(x = var_566_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_567_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_567_cast_fp16)[name = string("transpose_147")]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_557_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_578_perm_0 = const()[name = string("op_578_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_579 = const()[name = string("op_579"), val = tensor([1, 224, 512])]; - tensor var_578_cast_fp16 = transpose(perm = var_578_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; - tensor input_83_cast_fp16 = reshape(shape = var_579, x = var_578_cast_fp16)[name = string("input_83_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(13305600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13567808))))[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(13568896)))]; - tensor linear_22_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_83_cast_fp16)[name = string("linear_22_cast_fp16")]; - tensor var_585_cast_fp16 = mul(x = var_524_cast_fp16, y = linear_22_cast_fp16)[name = string("op_585_cast_fp16")]; - tensor input_87_cast_fp16 = add(x = input_81_cast_fp16, y = var_585_cast_fp16)[name = string("input_87_cast_fp16")]; - tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([1])]; - tensor var_587_cast_fp16 = expand_dims(axes = var_587_axes_0, x = var_518_cast_fp16_5)[name = string("op_587_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_69_to_fp16, x = input_87_cast_fp16)[name = string("x_19_cast_fp16")]; - tensor var_590_axes_0 = const()[name = string("op_590_axes_0"), val = tensor([1])]; - tensor var_590_cast_fp16 = expand_dims(axes = var_590_axes_0, x = var_518_cast_fp16_4)[name = string("op_590_cast_fp16")]; - fp16 var_591_to_fp16 = const()[name = string("op_591_to_fp16"), val = fp16(0x1p+0)]; - tensor var_592_cast_fp16 = add(x = var_590_cast_fp16, y = var_591_to_fp16)[name = string("op_592_cast_fp16")]; - tensor var_593_cast_fp16 = mul(x = x_19_cast_fp16, y = var_592_cast_fp16)[name = string("op_593_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_518_cast_fp16_3)[name = string("op_594_cast_fp16")]; - tensor input_89_cast_fp16 = add(x = var_593_cast_fp16, y = var_594_cast_fp16)[name = string("input_89_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(13569984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14618624))))[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(14622784)))]; - tensor linear_23_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_89_cast_fp16)[name = string("linear_23_cast_fp16")]; - string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")]; - tensor input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = linear_23_cast_fp16)[name = string("input_93_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(14626944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15675584))))[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(15676672)))]; - tensor linear_24_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_93_cast_fp16)[name = string("linear_24_cast_fp16")]; - tensor var_607_cast_fp16 = mul(x = var_587_cast_fp16, y = linear_24_cast_fp16)[name = string("op_607_cast_fp16")]; - tensor input_101_cast_fp16 = add(x = input_87_cast_fp16, y = var_607_cast_fp16)[name = string("input_101_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(15677760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17250688))))[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(17256896)))]; - tensor linear_25_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_39_cast_fp16)[name = string("linear_25_cast_fp16")]; - tensor var_616_split_sizes_0 = const()[name = string("op_616_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_616_axis_0 = const()[name = string("op_616_axis_0"), val = int32(-1)]; - tensor var_616_cast_fp16_0, tensor var_616_cast_fp16_1, tensor var_616_cast_fp16_2, tensor var_616_cast_fp16_3, tensor var_616_cast_fp16_4, tensor var_616_cast_fp16_5 = split(axis = var_616_axis_0, split_sizes = var_616_split_sizes_0, x = linear_25_cast_fp16)[name = string("op_616_cast_fp16")]; - tensor var_622_axes_0 = const()[name = string("op_622_axes_0"), val = tensor([1])]; - tensor var_622_cast_fp16 = expand_dims(axes = var_622_axes_0, x = var_616_cast_fp16_2)[name = string("op_622_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_69_to_fp16, x = input_101_cast_fp16)[name = string("x_21_cast_fp16")]; - tensor var_625_axes_0 = const()[name = string("op_625_axes_0"), val = tensor([1])]; - tensor var_625_cast_fp16 = expand_dims(axes = var_625_axes_0, x = var_616_cast_fp16_1)[name = string("op_625_cast_fp16")]; - fp16 var_626_to_fp16 = const()[name = string("op_626_to_fp16"), val = fp16(0x1p+0)]; - tensor var_627_cast_fp16 = add(x = var_625_cast_fp16, y = var_626_to_fp16)[name = string("op_627_cast_fp16")]; - tensor var_628_cast_fp16 = mul(x = x_21_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_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_616_cast_fp16_0)[name = string("op_629_cast_fp16")]; - tensor x_23_cast_fp16 = add(x = var_628_cast_fp16, y = var_629_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(17263104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18049600))))[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(18052736)))]; - tensor linear_26_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_26_cast_fp16")]; - tensor var_641 = const()[name = string("op_641"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_7_cast_fp16 = reshape(shape = var_641, x = linear_26_cast_fp16)[name = string("qkv_7_cast_fp16")]; - tensor var_643_split_sizes_0 = const()[name = string("op_643_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_643_axis_0 = const()[name = string("op_643_axis_0"), val = int32(2)]; - tensor var_643_cast_fp16_0, tensor var_643_cast_fp16_1, tensor var_643_cast_fp16_2 = split(axis = var_643_axis_0, split_sizes = var_643_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_643_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_643_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_643_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_643_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; - tensor var_648_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_648_cast_fp16")]; - tensor var_650_axes_0 = const()[name = string("op_650_axes_0"), val = tensor([-1])]; - bool var_650_keep_dims_0 = const()[name = string("op_650_keep_dims_0"), val = bool(true)]; - tensor var_650_cast_fp16 = reduce_mean(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = string("op_650_cast_fp16")]; - fp16 var_651_to_fp16 = const()[name = string("op_651_to_fp16"), val = fp16(0x1p-23)]; - tensor var_652_cast_fp16 = add(x = var_650_cast_fp16, y = var_651_to_fp16)[name = string("op_652_cast_fp16")]; - fp32 var_653_epsilon_0 = const()[name = string("op_653_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_653_cast_fp16 = rsqrt(epsilon = var_653_epsilon_0, x = var_652_cast_fp16)[name = string("op_653_cast_fp16")]; - tensor var_654_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_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(18055872)))]; - tensor var_655_cast_fp16 = mul(x = var_654_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_655_cast_fp16")]; - tensor var_658_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_658_cast_fp16")]; - tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; - bool var_660_keep_dims_0 = const()[name = string("op_660_keep_dims_0"), val = bool(true)]; - tensor var_660_cast_fp16 = reduce_mean(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = string("op_660_cast_fp16")]; - fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-23)]; - tensor var_662_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("op_662_cast_fp16")]; - fp32 var_663_epsilon_0 = const()[name = string("op_663_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_663_cast_fp16 = rsqrt(epsilon = var_663_epsilon_0, x = var_662_cast_fp16)[name = string("op_663_cast_fp16")]; - tensor var_664_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_663_cast_fp16)[name = string("op_664_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(18056064)))]; - tensor var_665_cast_fp16 = mul(x = var_664_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_665_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_665_cast_fp16)[name = string("transpose_143")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_655_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_676_perm_0 = const()[name = string("op_676_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_677 = const()[name = string("op_677"), val = tensor([1, 224, 512])]; - tensor var_676_cast_fp16 = transpose(perm = var_676_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; - tensor input_103_cast_fp16 = reshape(shape = var_677, x = var_676_cast_fp16)[name = string("input_103_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(18056256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18318464))))[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(18319552)))]; - tensor linear_27_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_103_cast_fp16)[name = string("linear_27_cast_fp16")]; - tensor var_683_cast_fp16 = mul(x = var_622_cast_fp16, y = linear_27_cast_fp16)[name = string("op_683_cast_fp16")]; - tensor input_107_cast_fp16 = add(x = input_101_cast_fp16, y = var_683_cast_fp16)[name = string("input_107_cast_fp16")]; - tensor var_685_axes_0 = const()[name = string("op_685_axes_0"), val = tensor([1])]; - tensor var_685_cast_fp16 = expand_dims(axes = var_685_axes_0, x = var_616_cast_fp16_5)[name = string("op_685_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_69_to_fp16, x = input_107_cast_fp16)[name = string("x_25_cast_fp16")]; - tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([1])]; - tensor var_688_cast_fp16 = expand_dims(axes = var_688_axes_0, x = var_616_cast_fp16_4)[name = string("op_688_cast_fp16")]; - fp16 var_689_to_fp16 = const()[name = string("op_689_to_fp16"), val = fp16(0x1p+0)]; - tensor var_690_cast_fp16 = add(x = var_688_cast_fp16, y = var_689_to_fp16)[name = string("op_690_cast_fp16")]; - tensor var_691_cast_fp16 = mul(x = x_25_cast_fp16, y = var_690_cast_fp16)[name = string("op_691_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_616_cast_fp16_3)[name = string("op_692_cast_fp16")]; - tensor input_109_cast_fp16 = add(x = var_691_cast_fp16, y = var_692_cast_fp16)[name = string("input_109_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(18320640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19369280))))[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(19373440)))]; - tensor linear_28_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_109_cast_fp16)[name = string("linear_28_cast_fp16")]; - string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; - tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_28_cast_fp16)[name = string("input_113_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(19377600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20426240))))[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(20427328)))]; - tensor linear_29_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_113_cast_fp16)[name = string("linear_29_cast_fp16")]; - tensor var_705_cast_fp16 = mul(x = var_685_cast_fp16, y = linear_29_cast_fp16)[name = string("op_705_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_107_cast_fp16, y = var_705_cast_fp16)[name = string("input_121_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(20428416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001344))))[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(22007552)))]; - tensor linear_30_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_39_cast_fp16)[name = string("linear_30_cast_fp16")]; - tensor var_714_split_sizes_0 = const()[name = string("op_714_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_714_axis_0 = const()[name = string("op_714_axis_0"), val = int32(-1)]; - tensor var_714_cast_fp16_0, tensor var_714_cast_fp16_1, tensor var_714_cast_fp16_2, tensor var_714_cast_fp16_3, tensor var_714_cast_fp16_4, tensor var_714_cast_fp16_5 = split(axis = var_714_axis_0, split_sizes = var_714_split_sizes_0, x = linear_30_cast_fp16)[name = string("op_714_cast_fp16")]; - tensor var_720_axes_0 = const()[name = string("op_720_axes_0"), val = tensor([1])]; - tensor var_720_cast_fp16 = expand_dims(axes = var_720_axes_0, x = var_714_cast_fp16_2)[name = string("op_720_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_69_to_fp16, x = input_121_cast_fp16)[name = string("x_27_cast_fp16")]; - tensor var_723_axes_0 = const()[name = string("op_723_axes_0"), val = tensor([1])]; - tensor var_723_cast_fp16 = expand_dims(axes = var_723_axes_0, x = var_714_cast_fp16_1)[name = string("op_723_cast_fp16")]; - fp16 var_724_to_fp16 = const()[name = string("op_724_to_fp16"), val = fp16(0x1p+0)]; - tensor var_725_cast_fp16 = add(x = var_723_cast_fp16, y = var_724_to_fp16)[name = string("op_725_cast_fp16")]; - tensor var_726_cast_fp16 = mul(x = x_27_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_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_714_cast_fp16_0)[name = string("op_727_cast_fp16")]; - tensor x_29_cast_fp16 = add(x = var_726_cast_fp16, y = var_727_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(22013760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22800256))))[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(22803392)))]; - tensor linear_31_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_31_cast_fp16")]; - tensor var_739 = const()[name = string("op_739"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_9_cast_fp16 = reshape(shape = var_739, x = linear_31_cast_fp16)[name = string("qkv_9_cast_fp16")]; - tensor var_741_split_sizes_0 = const()[name = string("op_741_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_741_axis_0 = const()[name = string("op_741_axis_0"), val = int32(2)]; - tensor var_741_cast_fp16_0, tensor var_741_cast_fp16_1, tensor var_741_cast_fp16_2 = split(axis = var_741_axis_0, split_sizes = var_741_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_741_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_741_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_741_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_741_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; - tensor var_746_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_746_cast_fp16")]; - tensor var_748_axes_0 = const()[name = string("op_748_axes_0"), val = tensor([-1])]; - bool var_748_keep_dims_0 = const()[name = string("op_748_keep_dims_0"), val = bool(true)]; - tensor var_748_cast_fp16 = reduce_mean(axes = var_748_axes_0, keep_dims = var_748_keep_dims_0, x = var_746_cast_fp16)[name = string("op_748_cast_fp16")]; - fp16 var_749_to_fp16 = const()[name = string("op_749_to_fp16"), val = fp16(0x1p-23)]; - tensor var_750_cast_fp16 = add(x = var_748_cast_fp16, y = var_749_to_fp16)[name = string("op_750_cast_fp16")]; - fp32 var_751_epsilon_0 = const()[name = string("op_751_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_751_cast_fp16 = rsqrt(epsilon = var_751_epsilon_0, x = var_750_cast_fp16)[name = string("op_751_cast_fp16")]; - tensor var_752_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_751_cast_fp16)[name = string("op_752_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(22806528)))]; - tensor var_753_cast_fp16 = mul(x = var_752_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_753_cast_fp16")]; - tensor var_756_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_756_cast_fp16")]; - tensor var_758_axes_0 = const()[name = string("op_758_axes_0"), val = tensor([-1])]; - bool var_758_keep_dims_0 = const()[name = string("op_758_keep_dims_0"), val = bool(true)]; - tensor var_758_cast_fp16 = reduce_mean(axes = var_758_axes_0, keep_dims = var_758_keep_dims_0, x = var_756_cast_fp16)[name = string("op_758_cast_fp16")]; - fp16 var_759_to_fp16 = const()[name = string("op_759_to_fp16"), val = fp16(0x1p-23)]; - tensor var_760_cast_fp16 = add(x = var_758_cast_fp16, y = var_759_to_fp16)[name = string("op_760_cast_fp16")]; - fp32 var_761_epsilon_0 = const()[name = string("op_761_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_761_cast_fp16 = rsqrt(epsilon = var_761_epsilon_0, x = var_760_cast_fp16)[name = string("op_761_cast_fp16")]; - tensor var_762_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_761_cast_fp16)[name = string("op_762_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(22806720)))]; - tensor var_763_cast_fp16 = mul(x = var_762_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_763_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_763_cast_fp16)[name = string("transpose_139")]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_753_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_774_perm_0 = const()[name = string("op_774_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_775 = const()[name = string("op_775"), val = tensor([1, 224, 512])]; - tensor var_774_cast_fp16 = transpose(perm = var_774_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; - tensor input_123_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("input_123_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(22806912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23069120))))[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(23070208)))]; - tensor linear_32_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_123_cast_fp16)[name = string("linear_32_cast_fp16")]; - tensor var_781_cast_fp16 = mul(x = var_720_cast_fp16, y = linear_32_cast_fp16)[name = string("op_781_cast_fp16")]; - tensor input_127_cast_fp16 = add(x = input_121_cast_fp16, y = var_781_cast_fp16)[name = string("input_127_cast_fp16")]; - tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([1])]; - tensor var_783_cast_fp16 = expand_dims(axes = var_783_axes_0, x = var_714_cast_fp16_5)[name = string("op_783_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_69_to_fp16, x = input_127_cast_fp16)[name = string("x_31_cast_fp16")]; - tensor var_786_axes_0 = const()[name = string("op_786_axes_0"), val = tensor([1])]; - tensor var_786_cast_fp16 = expand_dims(axes = var_786_axes_0, x = var_714_cast_fp16_4)[name = string("op_786_cast_fp16")]; - fp16 var_787_to_fp16 = const()[name = string("op_787_to_fp16"), val = fp16(0x1p+0)]; - tensor var_788_cast_fp16 = add(x = var_786_cast_fp16, y = var_787_to_fp16)[name = string("op_788_cast_fp16")]; - tensor var_789_cast_fp16 = mul(x = x_31_cast_fp16, y = var_788_cast_fp16)[name = string("op_789_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_714_cast_fp16_3)[name = string("op_790_cast_fp16")]; - tensor input_129_cast_fp16 = add(x = var_789_cast_fp16, y = var_790_cast_fp16)[name = string("input_129_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(23071296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119936))))[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(24124096)))]; - tensor linear_33_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_129_cast_fp16)[name = string("linear_33_cast_fp16")]; - string input_133_mode_0 = const()[name = string("input_133_mode_0"), val = string("EXACT")]; - tensor input_133_cast_fp16 = gelu(mode = input_133_mode_0, x = linear_33_cast_fp16)[name = string("input_133_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(24128256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176896))))[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(25177984)))]; - tensor linear_34_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_133_cast_fp16)[name = string("linear_34_cast_fp16")]; - tensor var_803_cast_fp16 = mul(x = var_783_cast_fp16, y = linear_34_cast_fp16)[name = string("op_803_cast_fp16")]; - tensor input_141_cast_fp16 = add(x = input_127_cast_fp16, y = var_803_cast_fp16)[name = string("input_141_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(25179072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26752000))))[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(26758208)))]; - tensor linear_35_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_39_cast_fp16)[name = string("linear_35_cast_fp16")]; - tensor var_812_split_sizes_0 = const()[name = string("op_812_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_812_axis_0 = const()[name = string("op_812_axis_0"), val = int32(-1)]; - tensor var_812_cast_fp16_0, tensor var_812_cast_fp16_1, tensor var_812_cast_fp16_2, tensor var_812_cast_fp16_3, tensor var_812_cast_fp16_4, tensor var_812_cast_fp16_5 = split(axis = var_812_axis_0, split_sizes = var_812_split_sizes_0, x = linear_35_cast_fp16)[name = string("op_812_cast_fp16")]; - tensor var_818_axes_0 = const()[name = string("op_818_axes_0"), val = tensor([1])]; - tensor var_818_cast_fp16 = expand_dims(axes = var_818_axes_0, x = var_812_cast_fp16_2)[name = string("op_818_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_69_to_fp16, x = input_141_cast_fp16)[name = string("x_33_cast_fp16")]; - tensor var_821_axes_0 = const()[name = string("op_821_axes_0"), val = tensor([1])]; - tensor var_821_cast_fp16 = expand_dims(axes = var_821_axes_0, x = var_812_cast_fp16_1)[name = string("op_821_cast_fp16")]; - fp16 var_822_to_fp16 = const()[name = string("op_822_to_fp16"), val = fp16(0x1p+0)]; - tensor var_823_cast_fp16 = add(x = var_821_cast_fp16, y = var_822_to_fp16)[name = string("op_823_cast_fp16")]; - tensor var_824_cast_fp16 = mul(x = x_33_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_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_812_cast_fp16_0)[name = string("op_825_cast_fp16")]; - tensor x_35_cast_fp16 = add(x = var_824_cast_fp16, y = var_825_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(26764416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27550912))))[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(27554048)))]; - tensor linear_36_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_36_cast_fp16")]; - tensor var_837 = const()[name = string("op_837"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_11_cast_fp16 = reshape(shape = var_837, x = linear_36_cast_fp16)[name = string("qkv_11_cast_fp16")]; - tensor var_839_split_sizes_0 = const()[name = string("op_839_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_839_axis_0 = const()[name = string("op_839_axis_0"), val = int32(2)]; - tensor var_839_cast_fp16_0, tensor var_839_cast_fp16_1, tensor var_839_cast_fp16_2 = split(axis = var_839_axis_0, split_sizes = var_839_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_839_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_839_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_839_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_839_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; - tensor var_844_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_844_cast_fp16")]; - tensor var_846_axes_0 = const()[name = string("op_846_axes_0"), val = tensor([-1])]; - bool var_846_keep_dims_0 = const()[name = string("op_846_keep_dims_0"), val = bool(true)]; - tensor var_846_cast_fp16 = reduce_mean(axes = var_846_axes_0, keep_dims = var_846_keep_dims_0, x = var_844_cast_fp16)[name = string("op_846_cast_fp16")]; - fp16 var_847_to_fp16 = const()[name = string("op_847_to_fp16"), val = fp16(0x1p-23)]; - tensor var_848_cast_fp16 = add(x = var_846_cast_fp16, y = var_847_to_fp16)[name = string("op_848_cast_fp16")]; - fp32 var_849_epsilon_0 = const()[name = string("op_849_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_849_cast_fp16 = rsqrt(epsilon = var_849_epsilon_0, x = var_848_cast_fp16)[name = string("op_849_cast_fp16")]; - tensor var_850_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_849_cast_fp16)[name = string("op_850_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(27557184)))]; - tensor var_851_cast_fp16 = mul(x = var_850_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_851_cast_fp16")]; - tensor var_854_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_854_cast_fp16")]; - tensor var_856_axes_0 = const()[name = string("op_856_axes_0"), val = tensor([-1])]; - bool var_856_keep_dims_0 = const()[name = string("op_856_keep_dims_0"), val = bool(true)]; - tensor var_856_cast_fp16 = reduce_mean(axes = var_856_axes_0, keep_dims = var_856_keep_dims_0, x = var_854_cast_fp16)[name = string("op_856_cast_fp16")]; - fp16 var_857_to_fp16 = const()[name = string("op_857_to_fp16"), val = fp16(0x1p-23)]; - tensor var_858_cast_fp16 = add(x = var_856_cast_fp16, y = var_857_to_fp16)[name = string("op_858_cast_fp16")]; - fp32 var_859_epsilon_0 = const()[name = string("op_859_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_859_cast_fp16 = rsqrt(epsilon = var_859_epsilon_0, x = var_858_cast_fp16)[name = string("op_859_cast_fp16")]; - tensor var_860_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_859_cast_fp16)[name = string("op_860_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(27557376)))]; - tensor var_861_cast_fp16 = mul(x = var_860_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_861_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_861_cast_fp16)[name = string("transpose_135")]; - tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_851_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_872_perm_0 = const()[name = string("op_872_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_873 = const()[name = string("op_873"), val = tensor([1, 224, 512])]; - tensor var_872_cast_fp16 = transpose(perm = var_872_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; - tensor input_143_cast_fp16 = reshape(shape = var_873, x = var_872_cast_fp16)[name = string("input_143_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(27557568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27819776))))[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(27820864)))]; - tensor linear_37_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_143_cast_fp16)[name = string("linear_37_cast_fp16")]; - tensor var_879_cast_fp16 = mul(x = var_818_cast_fp16, y = linear_37_cast_fp16)[name = string("op_879_cast_fp16")]; - tensor input_147_cast_fp16 = add(x = input_141_cast_fp16, y = var_879_cast_fp16)[name = string("input_147_cast_fp16")]; - tensor var_881_axes_0 = const()[name = string("op_881_axes_0"), val = tensor([1])]; - tensor var_881_cast_fp16 = expand_dims(axes = var_881_axes_0, x = var_812_cast_fp16_5)[name = string("op_881_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_69_to_fp16, x = input_147_cast_fp16)[name = string("x_37_cast_fp16")]; - tensor var_884_axes_0 = const()[name = string("op_884_axes_0"), val = tensor([1])]; - tensor var_884_cast_fp16 = expand_dims(axes = var_884_axes_0, x = var_812_cast_fp16_4)[name = string("op_884_cast_fp16")]; - fp16 var_885_to_fp16 = const()[name = string("op_885_to_fp16"), val = fp16(0x1p+0)]; - tensor var_886_cast_fp16 = add(x = var_884_cast_fp16, y = var_885_to_fp16)[name = string("op_886_cast_fp16")]; - tensor var_887_cast_fp16 = mul(x = x_37_cast_fp16, y = var_886_cast_fp16)[name = string("op_887_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_812_cast_fp16_3)[name = string("op_888_cast_fp16")]; - tensor input_149_cast_fp16 = add(x = var_887_cast_fp16, y = var_888_cast_fp16)[name = string("input_149_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(27821952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28870592))))[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(28874752)))]; - tensor linear_38_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_149_cast_fp16)[name = string("linear_38_cast_fp16")]; - string input_153_mode_0 = const()[name = string("input_153_mode_0"), val = string("EXACT")]; - tensor input_153_cast_fp16 = gelu(mode = input_153_mode_0, x = linear_38_cast_fp16)[name = string("input_153_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(28878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29927552))))[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(29928640)))]; - tensor linear_39_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_153_cast_fp16)[name = string("linear_39_cast_fp16")]; - tensor var_901_cast_fp16 = mul(x = var_881_cast_fp16, y = linear_39_cast_fp16)[name = string("op_901_cast_fp16")]; - tensor input_161_cast_fp16 = add(x = input_147_cast_fp16, y = var_901_cast_fp16)[name = string("input_161_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(29929728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31502656))))[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(31508864)))]; - tensor linear_40_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_39_cast_fp16)[name = string("linear_40_cast_fp16")]; - tensor var_910_split_sizes_0 = const()[name = string("op_910_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_910_axis_0 = const()[name = string("op_910_axis_0"), val = int32(-1)]; - tensor var_910_cast_fp16_0, tensor var_910_cast_fp16_1, tensor var_910_cast_fp16_2, tensor var_910_cast_fp16_3, tensor var_910_cast_fp16_4, tensor var_910_cast_fp16_5 = split(axis = var_910_axis_0, split_sizes = var_910_split_sizes_0, x = linear_40_cast_fp16)[name = string("op_910_cast_fp16")]; - tensor var_916_axes_0 = const()[name = string("op_916_axes_0"), val = tensor([1])]; - tensor var_916_cast_fp16 = expand_dims(axes = var_916_axes_0, x = var_910_cast_fp16_2)[name = string("op_916_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_69_to_fp16, x = input_161_cast_fp16)[name = string("x_39_cast_fp16")]; - tensor var_919_axes_0 = const()[name = string("op_919_axes_0"), val = tensor([1])]; - tensor var_919_cast_fp16 = expand_dims(axes = var_919_axes_0, x = var_910_cast_fp16_1)[name = string("op_919_cast_fp16")]; - fp16 var_920_to_fp16 = const()[name = string("op_920_to_fp16"), val = fp16(0x1p+0)]; - tensor var_921_cast_fp16 = add(x = var_919_cast_fp16, y = var_920_to_fp16)[name = string("op_921_cast_fp16")]; - tensor var_922_cast_fp16 = mul(x = x_39_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_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_910_cast_fp16_0)[name = string("op_923_cast_fp16")]; - tensor x_41_cast_fp16 = add(x = var_922_cast_fp16, y = var_923_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(31515072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32301568))))[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(32304704)))]; - tensor linear_41_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_41_cast_fp16")]; - tensor var_935 = const()[name = string("op_935"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_13_cast_fp16 = reshape(shape = var_935, x = linear_41_cast_fp16)[name = string("qkv_13_cast_fp16")]; - tensor var_937_split_sizes_0 = const()[name = string("op_937_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_937_axis_0 = const()[name = string("op_937_axis_0"), val = int32(2)]; - tensor var_937_cast_fp16_0, tensor var_937_cast_fp16_1, tensor var_937_cast_fp16_2 = split(axis = var_937_axis_0, split_sizes = var_937_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_937_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_937_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_937_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_937_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; - tensor var_942_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_942_cast_fp16")]; - tensor var_944_axes_0 = const()[name = string("op_944_axes_0"), val = tensor([-1])]; - bool var_944_keep_dims_0 = const()[name = string("op_944_keep_dims_0"), val = bool(true)]; - tensor var_944_cast_fp16 = reduce_mean(axes = var_944_axes_0, keep_dims = var_944_keep_dims_0, x = var_942_cast_fp16)[name = string("op_944_cast_fp16")]; - fp16 var_945_to_fp16 = const()[name = string("op_945_to_fp16"), val = fp16(0x1p-23)]; - tensor var_946_cast_fp16 = add(x = var_944_cast_fp16, y = var_945_to_fp16)[name = string("op_946_cast_fp16")]; - fp32 var_947_epsilon_0 = const()[name = string("op_947_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_947_cast_fp16 = rsqrt(epsilon = var_947_epsilon_0, x = var_946_cast_fp16)[name = string("op_947_cast_fp16")]; - tensor var_948_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_947_cast_fp16)[name = string("op_948_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(32307840)))]; - tensor var_949_cast_fp16 = mul(x = var_948_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_949_cast_fp16")]; - tensor var_952_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_952_cast_fp16")]; - tensor var_954_axes_0 = const()[name = string("op_954_axes_0"), val = tensor([-1])]; - bool var_954_keep_dims_0 = const()[name = string("op_954_keep_dims_0"), val = bool(true)]; - tensor var_954_cast_fp16 = reduce_mean(axes = var_954_axes_0, keep_dims = var_954_keep_dims_0, x = var_952_cast_fp16)[name = string("op_954_cast_fp16")]; - fp16 var_955_to_fp16 = const()[name = string("op_955_to_fp16"), val = fp16(0x1p-23)]; - tensor var_956_cast_fp16 = add(x = var_954_cast_fp16, y = var_955_to_fp16)[name = string("op_956_cast_fp16")]; - fp32 var_957_epsilon_0 = const()[name = string("op_957_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_957_cast_fp16 = rsqrt(epsilon = var_957_epsilon_0, x = var_956_cast_fp16)[name = string("op_957_cast_fp16")]; - tensor var_958_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_957_cast_fp16)[name = string("op_958_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(32308032)))]; - tensor var_959_cast_fp16 = mul(x = var_958_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_959_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_959_cast_fp16)[name = string("transpose_131")]; - tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_949_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_970_perm_0 = const()[name = string("op_970_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_971 = const()[name = string("op_971"), val = tensor([1, 224, 512])]; - tensor var_970_cast_fp16 = transpose(perm = var_970_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; - tensor input_163_cast_fp16 = reshape(shape = var_971, x = var_970_cast_fp16)[name = string("input_163_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(32308224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32570432))))[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(32571520)))]; - tensor linear_42_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_163_cast_fp16)[name = string("linear_42_cast_fp16")]; - tensor var_977_cast_fp16 = mul(x = var_916_cast_fp16, y = linear_42_cast_fp16)[name = string("op_977_cast_fp16")]; - tensor input_167_cast_fp16 = add(x = input_161_cast_fp16, y = var_977_cast_fp16)[name = string("input_167_cast_fp16")]; - tensor var_979_axes_0 = const()[name = string("op_979_axes_0"), val = tensor([1])]; - tensor var_979_cast_fp16 = expand_dims(axes = var_979_axes_0, x = var_910_cast_fp16_5)[name = string("op_979_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_69_to_fp16, x = input_167_cast_fp16)[name = string("x_43_cast_fp16")]; - tensor var_982_axes_0 = const()[name = string("op_982_axes_0"), val = tensor([1])]; - tensor var_982_cast_fp16 = expand_dims(axes = var_982_axes_0, x = var_910_cast_fp16_4)[name = string("op_982_cast_fp16")]; - fp16 var_983_to_fp16 = const()[name = string("op_983_to_fp16"), val = fp16(0x1p+0)]; - tensor var_984_cast_fp16 = add(x = var_982_cast_fp16, y = var_983_to_fp16)[name = string("op_984_cast_fp16")]; - tensor var_985_cast_fp16 = mul(x = x_43_cast_fp16, y = var_984_cast_fp16)[name = string("op_985_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_910_cast_fp16_3)[name = string("op_986_cast_fp16")]; - tensor input_169_cast_fp16 = add(x = var_985_cast_fp16, y = var_986_cast_fp16)[name = string("input_169_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(32572608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33621248))))[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(33625408)))]; - tensor linear_43_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_169_cast_fp16)[name = string("linear_43_cast_fp16")]; - string input_173_mode_0 = const()[name = string("input_173_mode_0"), val = string("EXACT")]; - tensor input_173_cast_fp16 = gelu(mode = input_173_mode_0, x = linear_43_cast_fp16)[name = string("input_173_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(33629568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34678208))))[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(34679296)))]; - tensor linear_44_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_173_cast_fp16)[name = string("linear_44_cast_fp16")]; - tensor var_999_cast_fp16 = mul(x = var_979_cast_fp16, y = linear_44_cast_fp16)[name = string("op_999_cast_fp16")]; - tensor input_181_cast_fp16 = add(x = input_167_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_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(34680384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36253312))))[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(36259520)))]; - tensor linear_45_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_39_cast_fp16)[name = string("linear_45_cast_fp16")]; - tensor var_1008_split_sizes_0 = const()[name = string("op_1008_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1008_axis_0 = const()[name = string("op_1008_axis_0"), val = int32(-1)]; - tensor var_1008_cast_fp16_0, tensor var_1008_cast_fp16_1, tensor var_1008_cast_fp16_2, tensor var_1008_cast_fp16_3, tensor var_1008_cast_fp16_4, tensor var_1008_cast_fp16_5 = split(axis = var_1008_axis_0, split_sizes = var_1008_split_sizes_0, x = linear_45_cast_fp16)[name = string("op_1008_cast_fp16")]; - tensor var_1014_axes_0 = const()[name = string("op_1014_axes_0"), val = tensor([1])]; - tensor var_1014_cast_fp16 = expand_dims(axes = var_1014_axes_0, x = var_1008_cast_fp16_2)[name = string("op_1014_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_69_to_fp16, x = input_181_cast_fp16)[name = string("x_45_cast_fp16")]; - tensor var_1017_axes_0 = const()[name = string("op_1017_axes_0"), val = tensor([1])]; - tensor var_1017_cast_fp16 = expand_dims(axes = var_1017_axes_0, x = var_1008_cast_fp16_1)[name = string("op_1017_cast_fp16")]; - fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1019_cast_fp16 = add(x = var_1017_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; - tensor var_1020_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_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_1008_cast_fp16_0)[name = string("op_1021_cast_fp16")]; - tensor x_47_cast_fp16 = add(x = var_1020_cast_fp16, y = var_1021_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(36265728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37052224))))[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(37055360)))]; - tensor linear_46_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_46_cast_fp16")]; - tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_15_cast_fp16 = reshape(shape = var_1033, x = linear_46_cast_fp16)[name = string("qkv_15_cast_fp16")]; - tensor var_1035_split_sizes_0 = const()[name = string("op_1035_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1035_axis_0 = const()[name = string("op_1035_axis_0"), val = int32(2)]; - tensor var_1035_cast_fp16_0, tensor var_1035_cast_fp16_1, tensor var_1035_cast_fp16_2 = split(axis = var_1035_axis_0, split_sizes = var_1035_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1035_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_1035_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_1035_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_1035_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; - tensor var_1040_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1040_cast_fp16")]; - tensor var_1042_axes_0 = const()[name = string("op_1042_axes_0"), val = tensor([-1])]; - bool var_1042_keep_dims_0 = const()[name = string("op_1042_keep_dims_0"), val = bool(true)]; - tensor var_1042_cast_fp16 = reduce_mean(axes = var_1042_axes_0, keep_dims = var_1042_keep_dims_0, x = var_1040_cast_fp16)[name = string("op_1042_cast_fp16")]; - fp16 var_1043_to_fp16 = const()[name = string("op_1043_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1044_cast_fp16 = add(x = var_1042_cast_fp16, y = var_1043_to_fp16)[name = string("op_1044_cast_fp16")]; - fp32 var_1045_epsilon_0 = const()[name = string("op_1045_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1045_cast_fp16 = rsqrt(epsilon = var_1045_epsilon_0, x = var_1044_cast_fp16)[name = string("op_1045_cast_fp16")]; - tensor var_1046_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1045_cast_fp16)[name = string("op_1046_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(37058496)))]; - tensor var_1047_cast_fp16 = mul(x = var_1046_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1047_cast_fp16")]; - tensor var_1050_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1050_cast_fp16")]; - tensor var_1052_axes_0 = const()[name = string("op_1052_axes_0"), val = tensor([-1])]; - bool var_1052_keep_dims_0 = const()[name = string("op_1052_keep_dims_0"), val = bool(true)]; - tensor var_1052_cast_fp16 = reduce_mean(axes = var_1052_axes_0, keep_dims = var_1052_keep_dims_0, x = var_1050_cast_fp16)[name = string("op_1052_cast_fp16")]; - fp16 var_1053_to_fp16 = const()[name = string("op_1053_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1054_cast_fp16 = add(x = var_1052_cast_fp16, y = var_1053_to_fp16)[name = string("op_1054_cast_fp16")]; - fp32 var_1055_epsilon_0 = const()[name = string("op_1055_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1055_cast_fp16 = rsqrt(epsilon = var_1055_epsilon_0, x = var_1054_cast_fp16)[name = string("op_1055_cast_fp16")]; - tensor var_1056_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1055_cast_fp16)[name = string("op_1056_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(37058688)))]; - tensor var_1057_cast_fp16 = mul(x = var_1056_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1057_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_1057_cast_fp16)[name = string("transpose_127")]; - tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1047_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_1068_perm_0 = const()[name = string("op_1068_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1069 = const()[name = string("op_1069"), val = tensor([1, 224, 512])]; - tensor var_1068_cast_fp16 = transpose(perm = var_1068_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; - tensor input_183_cast_fp16 = reshape(shape = var_1069, x = var_1068_cast_fp16)[name = string("input_183_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(37058880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37321088))))[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(37322176)))]; - tensor linear_47_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_183_cast_fp16)[name = string("linear_47_cast_fp16")]; - tensor var_1075_cast_fp16 = mul(x = var_1014_cast_fp16, y = linear_47_cast_fp16)[name = string("op_1075_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_181_cast_fp16, y = var_1075_cast_fp16)[name = string("input_187_cast_fp16")]; - tensor var_1077_axes_0 = const()[name = string("op_1077_axes_0"), val = tensor([1])]; - tensor var_1077_cast_fp16 = expand_dims(axes = var_1077_axes_0, x = var_1008_cast_fp16_5)[name = string("op_1077_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_69_to_fp16, x = input_187_cast_fp16)[name = string("x_49_cast_fp16")]; - tensor var_1080_axes_0 = const()[name = string("op_1080_axes_0"), val = tensor([1])]; - tensor var_1080_cast_fp16 = expand_dims(axes = var_1080_axes_0, x = var_1008_cast_fp16_4)[name = string("op_1080_cast_fp16")]; - fp16 var_1081_to_fp16 = const()[name = string("op_1081_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1082_cast_fp16 = add(x = var_1080_cast_fp16, y = var_1081_to_fp16)[name = string("op_1082_cast_fp16")]; - tensor var_1083_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1082_cast_fp16)[name = string("op_1083_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_1008_cast_fp16_3)[name = string("op_1084_cast_fp16")]; - tensor input_189_cast_fp16 = add(x = var_1083_cast_fp16, y = var_1084_cast_fp16)[name = string("input_189_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(37323264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38371904))))[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(38376064)))]; - tensor linear_48_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_189_cast_fp16)[name = string("linear_48_cast_fp16")]; - string input_193_mode_0 = const()[name = string("input_193_mode_0"), val = string("EXACT")]; - tensor input_193_cast_fp16 = gelu(mode = input_193_mode_0, x = linear_48_cast_fp16)[name = string("input_193_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(38380224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39428864))))[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(39429952)))]; - tensor linear_49_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_193_cast_fp16)[name = string("linear_49_cast_fp16")]; - tensor var_1097_cast_fp16 = mul(x = var_1077_cast_fp16, y = linear_49_cast_fp16)[name = string("op_1097_cast_fp16")]; - tensor input_201_cast_fp16 = add(x = input_187_cast_fp16, y = var_1097_cast_fp16)[name = string("input_201_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(39431040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41003968))))[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(41010176)))]; - tensor linear_50_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_39_cast_fp16)[name = string("linear_50_cast_fp16")]; - tensor var_1106_split_sizes_0 = const()[name = string("op_1106_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1106_axis_0 = const()[name = string("op_1106_axis_0"), val = int32(-1)]; - tensor var_1106_cast_fp16_0, tensor var_1106_cast_fp16_1, tensor var_1106_cast_fp16_2, tensor var_1106_cast_fp16_3, tensor var_1106_cast_fp16_4, tensor var_1106_cast_fp16_5 = split(axis = var_1106_axis_0, split_sizes = var_1106_split_sizes_0, x = linear_50_cast_fp16)[name = string("op_1106_cast_fp16")]; - tensor var_1112_axes_0 = const()[name = string("op_1112_axes_0"), val = tensor([1])]; - tensor var_1112_cast_fp16 = expand_dims(axes = var_1112_axes_0, x = var_1106_cast_fp16_2)[name = string("op_1112_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_69_to_fp16, x = input_201_cast_fp16)[name = string("x_51_cast_fp16")]; - tensor var_1115_axes_0 = const()[name = string("op_1115_axes_0"), val = tensor([1])]; - tensor var_1115_cast_fp16 = expand_dims(axes = var_1115_axes_0, x = var_1106_cast_fp16_1)[name = string("op_1115_cast_fp16")]; - fp16 var_1116_to_fp16 = const()[name = string("op_1116_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1117_cast_fp16 = add(x = var_1115_cast_fp16, y = var_1116_to_fp16)[name = string("op_1117_cast_fp16")]; - tensor var_1118_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1117_cast_fp16)[name = string("op_1118_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_1106_cast_fp16_0)[name = string("op_1119_cast_fp16")]; - tensor x_53_cast_fp16 = add(x = var_1118_cast_fp16, y = var_1119_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(41016384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41802880))))[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(41806016)))]; - tensor linear_51_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_51_cast_fp16")]; - tensor var_1131 = const()[name = string("op_1131"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_17_cast_fp16 = reshape(shape = var_1131, x = linear_51_cast_fp16)[name = string("qkv_17_cast_fp16")]; - tensor var_1133_split_sizes_0 = const()[name = string("op_1133_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1133_axis_0 = const()[name = string("op_1133_axis_0"), val = int32(2)]; - tensor var_1133_cast_fp16_0, tensor var_1133_cast_fp16_1, tensor var_1133_cast_fp16_2 = split(axis = var_1133_axis_0, split_sizes = var_1133_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1133_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_1133_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_1133_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_1133_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; - tensor var_1138_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1138_cast_fp16")]; - tensor var_1140_axes_0 = const()[name = string("op_1140_axes_0"), val = tensor([-1])]; - bool var_1140_keep_dims_0 = const()[name = string("op_1140_keep_dims_0"), val = bool(true)]; - tensor var_1140_cast_fp16 = reduce_mean(axes = var_1140_axes_0, keep_dims = var_1140_keep_dims_0, x = var_1138_cast_fp16)[name = string("op_1140_cast_fp16")]; - fp16 var_1141_to_fp16 = const()[name = string("op_1141_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1142_cast_fp16 = add(x = var_1140_cast_fp16, y = var_1141_to_fp16)[name = string("op_1142_cast_fp16")]; - fp32 var_1143_epsilon_0 = const()[name = string("op_1143_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1143_cast_fp16 = rsqrt(epsilon = var_1143_epsilon_0, x = var_1142_cast_fp16)[name = string("op_1143_cast_fp16")]; - tensor var_1144_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1143_cast_fp16)[name = string("op_1144_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(41809152)))]; - tensor var_1145_cast_fp16 = mul(x = var_1144_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1145_cast_fp16")]; - tensor var_1148_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1148_cast_fp16")]; - tensor var_1150_axes_0 = const()[name = string("op_1150_axes_0"), val = tensor([-1])]; - bool var_1150_keep_dims_0 = const()[name = string("op_1150_keep_dims_0"), val = bool(true)]; - tensor var_1150_cast_fp16 = reduce_mean(axes = var_1150_axes_0, keep_dims = var_1150_keep_dims_0, x = var_1148_cast_fp16)[name = string("op_1150_cast_fp16")]; - fp16 var_1151_to_fp16 = const()[name = string("op_1151_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1152_cast_fp16 = add(x = var_1150_cast_fp16, y = var_1151_to_fp16)[name = string("op_1152_cast_fp16")]; - fp32 var_1153_epsilon_0 = const()[name = string("op_1153_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1153_cast_fp16 = rsqrt(epsilon = var_1153_epsilon_0, x = var_1152_cast_fp16)[name = string("op_1153_cast_fp16")]; - tensor var_1154_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1153_cast_fp16)[name = string("op_1154_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(41809344)))]; - tensor var_1155_cast_fp16 = mul(x = var_1154_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1155_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_1155_cast_fp16)[name = string("transpose_123")]; - tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1145_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_1166_perm_0 = const()[name = string("op_1166_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1167 = const()[name = string("op_1167"), val = tensor([1, 224, 512])]; - tensor var_1166_cast_fp16 = transpose(perm = var_1166_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; - tensor input_203_cast_fp16 = reshape(shape = var_1167, x = var_1166_cast_fp16)[name = string("input_203_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(41809536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42071744))))[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(42072832)))]; - tensor linear_52_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_203_cast_fp16)[name = string("linear_52_cast_fp16")]; - tensor var_1173_cast_fp16 = mul(x = var_1112_cast_fp16, y = linear_52_cast_fp16)[name = string("op_1173_cast_fp16")]; - tensor input_207_cast_fp16 = add(x = input_201_cast_fp16, y = var_1173_cast_fp16)[name = string("input_207_cast_fp16")]; - tensor var_1175_axes_0 = const()[name = string("op_1175_axes_0"), val = tensor([1])]; - tensor var_1175_cast_fp16 = expand_dims(axes = var_1175_axes_0, x = var_1106_cast_fp16_5)[name = string("op_1175_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_69_to_fp16, x = input_207_cast_fp16)[name = string("x_55_cast_fp16")]; - tensor var_1178_axes_0 = const()[name = string("op_1178_axes_0"), val = tensor([1])]; - tensor var_1178_cast_fp16 = expand_dims(axes = var_1178_axes_0, x = var_1106_cast_fp16_4)[name = string("op_1178_cast_fp16")]; - fp16 var_1179_to_fp16 = const()[name = string("op_1179_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1180_cast_fp16 = add(x = var_1178_cast_fp16, y = var_1179_to_fp16)[name = string("op_1180_cast_fp16")]; - tensor var_1181_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1180_cast_fp16)[name = string("op_1181_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_1106_cast_fp16_3)[name = string("op_1182_cast_fp16")]; - tensor input_209_cast_fp16 = add(x = var_1181_cast_fp16, y = var_1182_cast_fp16)[name = string("input_209_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(42073920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43122560))))[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(43126720)))]; - tensor linear_53_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_209_cast_fp16)[name = string("linear_53_cast_fp16")]; - string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("EXACT")]; - tensor input_213_cast_fp16 = gelu(mode = input_213_mode_0, x = linear_53_cast_fp16)[name = string("input_213_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(43130880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44179520))))[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(44180608)))]; - tensor linear_54_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_213_cast_fp16)[name = string("linear_54_cast_fp16")]; - tensor var_1195_cast_fp16 = mul(x = var_1175_cast_fp16, y = linear_54_cast_fp16)[name = string("op_1195_cast_fp16")]; - tensor input_221_cast_fp16 = add(x = input_207_cast_fp16, y = var_1195_cast_fp16)[name = string("input_221_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(44181696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45754624))))[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(45760832)))]; - tensor linear_55_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_39_cast_fp16)[name = string("linear_55_cast_fp16")]; - tensor var_1204_split_sizes_0 = const()[name = string("op_1204_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1204_axis_0 = const()[name = string("op_1204_axis_0"), val = int32(-1)]; - tensor var_1204_cast_fp16_0, tensor var_1204_cast_fp16_1, tensor var_1204_cast_fp16_2, tensor var_1204_cast_fp16_3, tensor var_1204_cast_fp16_4, tensor var_1204_cast_fp16_5 = split(axis = var_1204_axis_0, split_sizes = var_1204_split_sizes_0, x = linear_55_cast_fp16)[name = string("op_1204_cast_fp16")]; - tensor var_1210_axes_0 = const()[name = string("op_1210_axes_0"), val = tensor([1])]; - tensor var_1210_cast_fp16 = expand_dims(axes = var_1210_axes_0, x = var_1204_cast_fp16_2)[name = string("op_1210_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_69_to_fp16, x = input_221_cast_fp16)[name = string("x_57_cast_fp16")]; - tensor var_1213_axes_0 = const()[name = string("op_1213_axes_0"), val = tensor([1])]; - tensor var_1213_cast_fp16 = expand_dims(axes = var_1213_axes_0, x = var_1204_cast_fp16_1)[name = string("op_1213_cast_fp16")]; - fp16 var_1214_to_fp16 = const()[name = string("op_1214_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1215_cast_fp16 = add(x = var_1213_cast_fp16, y = var_1214_to_fp16)[name = string("op_1215_cast_fp16")]; - tensor var_1216_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1215_cast_fp16)[name = string("op_1216_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_1204_cast_fp16_0)[name = string("op_1217_cast_fp16")]; - tensor x_59_cast_fp16 = add(x = var_1216_cast_fp16, y = var_1217_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(45767040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46553536))))[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(46556672)))]; - tensor linear_56_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_56_cast_fp16")]; - tensor var_1229 = const()[name = string("op_1229"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_19_cast_fp16 = reshape(shape = var_1229, x = linear_56_cast_fp16)[name = string("qkv_19_cast_fp16")]; - tensor var_1231_split_sizes_0 = const()[name = string("op_1231_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1231_axis_0 = const()[name = string("op_1231_axis_0"), val = int32(2)]; - tensor var_1231_cast_fp16_0, tensor var_1231_cast_fp16_1, tensor var_1231_cast_fp16_2 = split(axis = var_1231_axis_0, split_sizes = var_1231_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1231_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_1231_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_1231_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_1231_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; - tensor var_1236_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1236_cast_fp16")]; - tensor var_1238_axes_0 = const()[name = string("op_1238_axes_0"), val = tensor([-1])]; - bool var_1238_keep_dims_0 = const()[name = string("op_1238_keep_dims_0"), val = bool(true)]; - tensor var_1238_cast_fp16 = reduce_mean(axes = var_1238_axes_0, keep_dims = var_1238_keep_dims_0, x = var_1236_cast_fp16)[name = string("op_1238_cast_fp16")]; - fp16 var_1239_to_fp16 = const()[name = string("op_1239_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1240_cast_fp16 = add(x = var_1238_cast_fp16, y = var_1239_to_fp16)[name = string("op_1240_cast_fp16")]; - fp32 var_1241_epsilon_0 = const()[name = string("op_1241_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1241_cast_fp16 = rsqrt(epsilon = var_1241_epsilon_0, x = var_1240_cast_fp16)[name = string("op_1241_cast_fp16")]; - tensor var_1242_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1241_cast_fp16)[name = string("op_1242_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(46559808)))]; - tensor var_1243_cast_fp16 = mul(x = var_1242_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1243_cast_fp16")]; - tensor var_1246_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1246_cast_fp16")]; - tensor var_1248_axes_0 = const()[name = string("op_1248_axes_0"), val = tensor([-1])]; - bool var_1248_keep_dims_0 = const()[name = string("op_1248_keep_dims_0"), val = bool(true)]; - tensor var_1248_cast_fp16 = reduce_mean(axes = var_1248_axes_0, keep_dims = var_1248_keep_dims_0, x = var_1246_cast_fp16)[name = string("op_1248_cast_fp16")]; - fp16 var_1249_to_fp16 = const()[name = string("op_1249_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1250_cast_fp16 = add(x = var_1248_cast_fp16, y = var_1249_to_fp16)[name = string("op_1250_cast_fp16")]; - fp32 var_1251_epsilon_0 = const()[name = string("op_1251_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1251_cast_fp16 = rsqrt(epsilon = var_1251_epsilon_0, x = var_1250_cast_fp16)[name = string("op_1251_cast_fp16")]; - tensor var_1252_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1251_cast_fp16)[name = string("op_1252_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(46560000)))]; - tensor var_1253_cast_fp16 = mul(x = var_1252_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1253_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_1253_cast_fp16)[name = string("transpose_119")]; - tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1243_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_1264_perm_0 = const()[name = string("op_1264_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1265 = const()[name = string("op_1265"), val = tensor([1, 224, 512])]; - tensor var_1264_cast_fp16 = transpose(perm = var_1264_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; - tensor input_223_cast_fp16 = reshape(shape = var_1265, x = var_1264_cast_fp16)[name = string("input_223_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(46560192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46822400))))[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(46823488)))]; - tensor linear_57_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_223_cast_fp16)[name = string("linear_57_cast_fp16")]; - tensor var_1271_cast_fp16 = mul(x = var_1210_cast_fp16, y = linear_57_cast_fp16)[name = string("op_1271_cast_fp16")]; - tensor input_227_cast_fp16 = add(x = input_221_cast_fp16, y = var_1271_cast_fp16)[name = string("input_227_cast_fp16")]; - tensor var_1273_axes_0 = const()[name = string("op_1273_axes_0"), val = tensor([1])]; - tensor var_1273_cast_fp16 = expand_dims(axes = var_1273_axes_0, x = var_1204_cast_fp16_5)[name = string("op_1273_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_69_to_fp16, x = input_227_cast_fp16)[name = string("x_61_cast_fp16")]; - tensor var_1276_axes_0 = const()[name = string("op_1276_axes_0"), val = tensor([1])]; - tensor var_1276_cast_fp16 = expand_dims(axes = var_1276_axes_0, x = var_1204_cast_fp16_4)[name = string("op_1276_cast_fp16")]; - fp16 var_1277_to_fp16 = const()[name = string("op_1277_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1278_cast_fp16 = add(x = var_1276_cast_fp16, y = var_1277_to_fp16)[name = string("op_1278_cast_fp16")]; - tensor var_1279_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1278_cast_fp16)[name = string("op_1279_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_1204_cast_fp16_3)[name = string("op_1280_cast_fp16")]; - tensor input_229_cast_fp16 = add(x = var_1279_cast_fp16, y = var_1280_cast_fp16)[name = string("input_229_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(46824576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47873216))))[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(47877376)))]; - tensor linear_58_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_229_cast_fp16)[name = string("linear_58_cast_fp16")]; - string input_233_mode_0 = const()[name = string("input_233_mode_0"), val = string("EXACT")]; - tensor input_233_cast_fp16 = gelu(mode = input_233_mode_0, x = linear_58_cast_fp16)[name = string("input_233_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(47881536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48930176))))[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(48931264)))]; - tensor linear_59_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_233_cast_fp16)[name = string("linear_59_cast_fp16")]; - tensor var_1293_cast_fp16 = mul(x = var_1273_cast_fp16, y = linear_59_cast_fp16)[name = string("op_1293_cast_fp16")]; - tensor input_241_cast_fp16 = add(x = input_227_cast_fp16, y = var_1293_cast_fp16)[name = string("input_241_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(48932352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50505280))))[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(50511488)))]; - tensor linear_60_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_39_cast_fp16)[name = string("linear_60_cast_fp16")]; - tensor var_1302_split_sizes_0 = const()[name = string("op_1302_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1302_axis_0 = const()[name = string("op_1302_axis_0"), val = int32(-1)]; - tensor var_1302_cast_fp16_0, tensor var_1302_cast_fp16_1, tensor var_1302_cast_fp16_2, tensor var_1302_cast_fp16_3, tensor var_1302_cast_fp16_4, tensor var_1302_cast_fp16_5 = split(axis = var_1302_axis_0, split_sizes = var_1302_split_sizes_0, x = linear_60_cast_fp16)[name = string("op_1302_cast_fp16")]; - tensor var_1308_axes_0 = const()[name = string("op_1308_axes_0"), val = tensor([1])]; - tensor var_1308_cast_fp16 = expand_dims(axes = var_1308_axes_0, x = var_1302_cast_fp16_2)[name = string("op_1308_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_69_to_fp16, x = input_241_cast_fp16)[name = string("x_63_cast_fp16")]; - tensor var_1311_axes_0 = const()[name = string("op_1311_axes_0"), val = tensor([1])]; - tensor var_1311_cast_fp16 = expand_dims(axes = var_1311_axes_0, x = var_1302_cast_fp16_1)[name = string("op_1311_cast_fp16")]; - fp16 var_1312_to_fp16 = const()[name = string("op_1312_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1313_cast_fp16 = add(x = var_1311_cast_fp16, y = var_1312_to_fp16)[name = string("op_1313_cast_fp16")]; - tensor var_1314_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1313_cast_fp16)[name = string("op_1314_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_1302_cast_fp16_0)[name = string("op_1315_cast_fp16")]; - tensor x_65_cast_fp16 = add(x = var_1314_cast_fp16, y = var_1315_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(50517696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51304192))))[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(51307328)))]; - tensor linear_61_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_61_cast_fp16")]; - tensor var_1327 = const()[name = string("op_1327"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_21_cast_fp16 = reshape(shape = var_1327, x = linear_61_cast_fp16)[name = string("qkv_21_cast_fp16")]; - tensor var_1329_split_sizes_0 = const()[name = string("op_1329_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1329_axis_0 = const()[name = string("op_1329_axis_0"), val = int32(2)]; - tensor var_1329_cast_fp16_0, tensor var_1329_cast_fp16_1, tensor var_1329_cast_fp16_2 = split(axis = var_1329_axis_0, split_sizes = var_1329_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1329_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_1329_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_1329_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_1329_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; - tensor var_1334_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1334_cast_fp16")]; - tensor var_1336_axes_0 = const()[name = string("op_1336_axes_0"), val = tensor([-1])]; - bool var_1336_keep_dims_0 = const()[name = string("op_1336_keep_dims_0"), val = bool(true)]; - tensor var_1336_cast_fp16 = reduce_mean(axes = var_1336_axes_0, keep_dims = var_1336_keep_dims_0, x = var_1334_cast_fp16)[name = string("op_1336_cast_fp16")]; - fp16 var_1337_to_fp16 = const()[name = string("op_1337_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1338_cast_fp16 = add(x = var_1336_cast_fp16, y = var_1337_to_fp16)[name = string("op_1338_cast_fp16")]; - fp32 var_1339_epsilon_0 = const()[name = string("op_1339_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1339_cast_fp16 = rsqrt(epsilon = var_1339_epsilon_0, x = var_1338_cast_fp16)[name = string("op_1339_cast_fp16")]; - tensor var_1340_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1339_cast_fp16)[name = string("op_1340_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(51310464)))]; - tensor var_1341_cast_fp16 = mul(x = var_1340_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1341_cast_fp16")]; - tensor var_1344_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1344_cast_fp16")]; - tensor var_1346_axes_0 = const()[name = string("op_1346_axes_0"), val = tensor([-1])]; - bool var_1346_keep_dims_0 = const()[name = string("op_1346_keep_dims_0"), val = bool(true)]; - tensor var_1346_cast_fp16 = reduce_mean(axes = var_1346_axes_0, keep_dims = var_1346_keep_dims_0, x = var_1344_cast_fp16)[name = string("op_1346_cast_fp16")]; - fp16 var_1347_to_fp16 = const()[name = string("op_1347_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1348_cast_fp16 = add(x = var_1346_cast_fp16, y = var_1347_to_fp16)[name = string("op_1348_cast_fp16")]; - fp32 var_1349_epsilon_0 = const()[name = string("op_1349_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1349_cast_fp16 = rsqrt(epsilon = var_1349_epsilon_0, x = var_1348_cast_fp16)[name = string("op_1349_cast_fp16")]; - tensor var_1350_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1349_cast_fp16)[name = string("op_1350_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(51310656)))]; - tensor var_1351_cast_fp16 = mul(x = var_1350_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1351_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_1351_cast_fp16)[name = string("transpose_115")]; - tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1341_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_1362_perm_0 = const()[name = string("op_1362_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1363 = const()[name = string("op_1363"), val = tensor([1, 224, 512])]; - tensor var_1362_cast_fp16 = transpose(perm = var_1362_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; - tensor input_243_cast_fp16 = reshape(shape = var_1363, x = var_1362_cast_fp16)[name = string("input_243_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(51310848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51573056))))[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(51574144)))]; - tensor linear_62_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_243_cast_fp16)[name = string("linear_62_cast_fp16")]; - tensor var_1369_cast_fp16 = mul(x = var_1308_cast_fp16, y = linear_62_cast_fp16)[name = string("op_1369_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_241_cast_fp16, y = var_1369_cast_fp16)[name = string("input_247_cast_fp16")]; - tensor var_1371_axes_0 = const()[name = string("op_1371_axes_0"), val = tensor([1])]; - tensor var_1371_cast_fp16 = expand_dims(axes = var_1371_axes_0, x = var_1302_cast_fp16_5)[name = string("op_1371_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_69_to_fp16, x = input_247_cast_fp16)[name = string("x_67_cast_fp16")]; - tensor var_1374_axes_0 = const()[name = string("op_1374_axes_0"), val = tensor([1])]; - tensor var_1374_cast_fp16 = expand_dims(axes = var_1374_axes_0, x = var_1302_cast_fp16_4)[name = string("op_1374_cast_fp16")]; - fp16 var_1375_to_fp16 = const()[name = string("op_1375_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1376_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1375_to_fp16)[name = string("op_1376_cast_fp16")]; - tensor var_1377_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1376_cast_fp16)[name = string("op_1377_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_1302_cast_fp16_3)[name = string("op_1378_cast_fp16")]; - tensor input_249_cast_fp16 = add(x = var_1377_cast_fp16, y = var_1378_cast_fp16)[name = string("input_249_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(51575232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52623872))))[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(52628032)))]; - tensor linear_63_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_249_cast_fp16)[name = string("linear_63_cast_fp16")]; - string input_253_mode_0 = const()[name = string("input_253_mode_0"), val = string("EXACT")]; - tensor input_253_cast_fp16 = gelu(mode = input_253_mode_0, x = linear_63_cast_fp16)[name = string("input_253_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(52632192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53680832))))[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(53681920)))]; - tensor linear_64_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_253_cast_fp16)[name = string("linear_64_cast_fp16")]; - tensor var_1391_cast_fp16 = mul(x = var_1371_cast_fp16, y = linear_64_cast_fp16)[name = string("op_1391_cast_fp16")]; - tensor input_261_cast_fp16 = add(x = input_247_cast_fp16, y = var_1391_cast_fp16)[name = string("input_261_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(53683008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55255936))))[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(55262144)))]; - tensor linear_65_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_39_cast_fp16)[name = string("linear_65_cast_fp16")]; - tensor var_1400_split_sizes_0 = const()[name = string("op_1400_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1400_axis_0 = const()[name = string("op_1400_axis_0"), val = int32(-1)]; - tensor var_1400_cast_fp16_0, tensor var_1400_cast_fp16_1, tensor var_1400_cast_fp16_2, tensor var_1400_cast_fp16_3, tensor var_1400_cast_fp16_4, tensor var_1400_cast_fp16_5 = split(axis = var_1400_axis_0, split_sizes = var_1400_split_sizes_0, x = linear_65_cast_fp16)[name = string("op_1400_cast_fp16")]; - tensor var_1406_axes_0 = const()[name = string("op_1406_axes_0"), val = tensor([1])]; - tensor var_1406_cast_fp16 = expand_dims(axes = var_1406_axes_0, x = var_1400_cast_fp16_2)[name = string("op_1406_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_69_to_fp16, x = input_261_cast_fp16)[name = string("x_69_cast_fp16")]; - tensor var_1409_axes_0 = const()[name = string("op_1409_axes_0"), val = tensor([1])]; - tensor var_1409_cast_fp16 = expand_dims(axes = var_1409_axes_0, x = var_1400_cast_fp16_1)[name = string("op_1409_cast_fp16")]; - fp16 var_1410_to_fp16 = const()[name = string("op_1410_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1411_cast_fp16 = add(x = var_1409_cast_fp16, y = var_1410_to_fp16)[name = string("op_1411_cast_fp16")]; - tensor var_1412_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1411_cast_fp16)[name = string("op_1412_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_1400_cast_fp16_0)[name = string("op_1413_cast_fp16")]; - tensor x_71_cast_fp16 = add(x = var_1412_cast_fp16, y = var_1413_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(55268352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56054848))))[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(56057984)))]; - tensor linear_66_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_66_cast_fp16")]; - tensor var_1425 = const()[name = string("op_1425"), val = tensor([1, 224, 3, 8, 64])]; - tensor qkv_23_cast_fp16 = reshape(shape = var_1425, x = linear_66_cast_fp16)[name = string("qkv_23_cast_fp16")]; - tensor var_1427_split_sizes_0 = const()[name = string("op_1427_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1427_axis_0 = const()[name = string("op_1427_axis_0"), val = int32(2)]; - tensor var_1427_cast_fp16_0, tensor var_1427_cast_fp16_1, tensor var_1427_cast_fp16_2 = split(axis = var_1427_axis_0, split_sizes = var_1427_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1427_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_1427_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_1427_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_1427_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; - tensor var_1432_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1432_cast_fp16")]; - tensor var_1434_axes_0 = const()[name = string("op_1434_axes_0"), val = tensor([-1])]; - bool var_1434_keep_dims_0 = const()[name = string("op_1434_keep_dims_0"), val = bool(true)]; - tensor var_1434_cast_fp16 = reduce_mean(axes = var_1434_axes_0, keep_dims = var_1434_keep_dims_0, x = var_1432_cast_fp16)[name = string("op_1434_cast_fp16")]; - fp16 var_1435_to_fp16 = const()[name = string("op_1435_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1436_cast_fp16 = add(x = var_1434_cast_fp16, y = var_1435_to_fp16)[name = string("op_1436_cast_fp16")]; - fp32 var_1437_epsilon_0 = const()[name = string("op_1437_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1437_cast_fp16 = rsqrt(epsilon = var_1437_epsilon_0, x = var_1436_cast_fp16)[name = string("op_1437_cast_fp16")]; - tensor var_1438_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1437_cast_fp16)[name = string("op_1438_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(56061120)))]; - tensor var_1439_cast_fp16 = mul(x = var_1438_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1439_cast_fp16")]; - tensor var_1442_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1442_cast_fp16")]; - tensor var_1444_axes_0 = const()[name = string("op_1444_axes_0"), val = tensor([-1])]; - bool var_1444_keep_dims_0 = const()[name = string("op_1444_keep_dims_0"), val = bool(true)]; - tensor var_1444_cast_fp16 = reduce_mean(axes = var_1444_axes_0, keep_dims = var_1444_keep_dims_0, x = var_1442_cast_fp16)[name = string("op_1444_cast_fp16")]; - fp16 var_1445_to_fp16 = const()[name = string("op_1445_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1446_cast_fp16 = add(x = var_1444_cast_fp16, y = var_1445_to_fp16)[name = string("op_1446_cast_fp16")]; - fp32 var_1447_epsilon_0 = const()[name = string("op_1447_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1447_cast_fp16 = rsqrt(epsilon = var_1447_epsilon_0, x = var_1446_cast_fp16)[name = string("op_1447_cast_fp16")]; - tensor var_1448_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1447_cast_fp16)[name = string("op_1448_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(56061312)))]; - tensor var_1449_cast_fp16 = mul(x = var_1448_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1449_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_1449_cast_fp16)[name = string("transpose_111")]; - tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1439_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_1460_perm_0 = const()[name = string("op_1460_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1461 = const()[name = string("op_1461"), val = tensor([1, 224, 512])]; - tensor var_1460_cast_fp16 = transpose(perm = var_1460_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; - tensor input_263_cast_fp16 = reshape(shape = var_1461, x = var_1460_cast_fp16)[name = string("input_263_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(56061504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56323712))))[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(56324800)))]; - tensor linear_67_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_263_cast_fp16)[name = string("linear_67_cast_fp16")]; - tensor var_1467_cast_fp16 = mul(x = var_1406_cast_fp16, y = linear_67_cast_fp16)[name = string("op_1467_cast_fp16")]; - tensor input_267_cast_fp16 = add(x = input_261_cast_fp16, y = var_1467_cast_fp16)[name = string("input_267_cast_fp16")]; - tensor var_1469_axes_0 = const()[name = string("op_1469_axes_0"), val = tensor([1])]; - tensor var_1469_cast_fp16 = expand_dims(axes = var_1469_axes_0, x = var_1400_cast_fp16_5)[name = string("op_1469_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_69_to_fp16, x = input_267_cast_fp16)[name = string("x_73_cast_fp16")]; - tensor var_1472_axes_0 = const()[name = string("op_1472_axes_0"), val = tensor([1])]; - tensor var_1472_cast_fp16 = expand_dims(axes = var_1472_axes_0, x = var_1400_cast_fp16_4)[name = string("op_1472_cast_fp16")]; - fp16 var_1473_to_fp16 = const()[name = string("op_1473_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1474_cast_fp16 = add(x = var_1472_cast_fp16, y = var_1473_to_fp16)[name = string("op_1474_cast_fp16")]; - tensor var_1475_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1474_cast_fp16)[name = string("op_1475_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_1400_cast_fp16_3)[name = string("op_1476_cast_fp16")]; - tensor input_269_cast_fp16 = add(x = var_1475_cast_fp16, y = var_1476_cast_fp16)[name = string("input_269_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(56325888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57374528))))[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(57378688)))]; - tensor linear_68_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_269_cast_fp16)[name = string("linear_68_cast_fp16")]; - string input_273_mode_0 = const()[name = string("input_273_mode_0"), val = string("EXACT")]; - tensor input_273_cast_fp16 = gelu(mode = input_273_mode_0, x = linear_68_cast_fp16)[name = string("input_273_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(57382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58431488))))[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(58432576)))]; - tensor linear_69_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_273_cast_fp16)[name = string("linear_69_cast_fp16")]; - tensor var_1489_cast_fp16 = mul(x = var_1469_cast_fp16, y = linear_69_cast_fp16)[name = string("op_1489_cast_fp16")]; - tensor input_281_cast_fp16 = add(x = input_267_cast_fp16, y = var_1489_cast_fp16)[name = string("input_281_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(58433664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58958016))))[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(58960128)))]; - tensor linear_70_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_39_cast_fp16)[name = string("linear_70_cast_fp16")]; - tensor var_1495_split_sizes_0 = const()[name = string("op_1495_split_sizes_0"), val = tensor([512, 512])]; - int32 var_1495_axis_0 = const()[name = string("op_1495_axis_0"), val = int32(-1)]; - tensor var_1495_cast_fp16_0, tensor var_1495_cast_fp16_1 = split(axis = var_1495_axis_0, split_sizes = var_1495_split_sizes_0, x = linear_70_cast_fp16)[name = string("op_1495_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_69_to_fp16, x = input_281_cast_fp16)[name = string("x_cast_fp16")]; - tensor var_1499_axes_0 = const()[name = string("op_1499_axes_0"), val = tensor([1])]; - tensor var_1499_cast_fp16 = expand_dims(axes = var_1499_axes_0, x = var_1495_cast_fp16_1)[name = string("op_1499_cast_fp16")]; - fp16 var_1500_to_fp16 = const()[name = string("op_1500_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1501_cast_fp16 = add(x = var_1499_cast_fp16, y = var_1500_to_fp16)[name = string("op_1501_cast_fp16")]; - tensor var_1502_cast_fp16 = mul(x = x_cast_fp16, y = var_1501_cast_fp16)[name = string("op_1502_cast_fp16")]; - tensor var_1503_axes_0 = const()[name = string("op_1503_axes_0"), val = tensor([1])]; - tensor var_1503_cast_fp16 = expand_dims(axes = var_1503_axes_0, x = var_1495_cast_fp16_0)[name = string("op_1503_cast_fp16")]; - tensor encoded_cast_fp16 = add(x = var_1502_cast_fp16, y = var_1503_cast_fp16)[name = string("encoded_cast_fp16")]; - tensor input_283_begin_0 = const()[name = string("input_283_begin_0"), val = tensor([0, 96, 0])]; - tensor input_283_end_0 = const()[name = string("input_283_end_0"), val = tensor([1, 224, 512])]; - tensor input_283_end_mask_0 = const()[name = string("input_283_end_mask_0"), val = tensor([true, true, true])]; - tensor input_283_cast_fp16 = slice_by_index(begin = input_283_begin_0, end = input_283_end_0, end_mask = input_283_end_mask_0, x = encoded_cast_fp16)[name = string("input_283_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(58962240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59224448))))[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(59225536)))]; - tensor linear_71_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_71_cast_fp16")]; - string input_287_mode_0 = const()[name = string("input_287_mode_0"), val = string("EXACT")]; - tensor input_287_cast_fp16 = gelu(mode = input_287_mode_0, x = linear_71_cast_fp16)[name = string("input_287_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(59226624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59238976))))[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(59239104)))]; - tensor linear_72_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_72_cast_fp16")]; - tensor var_1517_perm_0 = const()[name = string("op_1517_perm_0"), val = tensor([0, 2, 1])]; - string var_1517_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1517_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; - tensor var_1517_cast_fp16 = transpose(perm = var_1517_perm_0, x = linear_72_cast_fp16)[name = string("transpose_108")]; - tensor velocity = cast(dtype = var_1517_cast_fp16_to_fp32_dtype_0, x = var_1517_cast_fp16)[name = string("cast_1")]; - } -> (velocity); - func vector_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 prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { - int32 var_33 = const()[name = string("op_33"), val = int32(0)]; - tensor present = not_equal(x = delivery_present, y = var_33)[name = string("present")]; - int32 var_35 = const()[name = string("op_35"), val = int32(0)]; - tensor latent_mask_1 = not_equal(x = latent_mask, y = var_35)[name = string("latent_mask")]; - int32 var_37 = const()[name = string("op_37"), val = int32(0)]; - tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_37)[name = string("prefix_mask_1")]; - int32 var_58 = const()[name = string("op_58"), val = int32(-1)]; - int32 var_73 = const()[name = string("op_73"), val = int32(1)]; - int32 var_87 = const()[name = string("op_87"), val = int32(7)]; - tensor var_88 = mul(x = voice_id, y = var_87)[name = string("op_88")]; - tensor index = add(x = var_88, 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(35968))))[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_20")]; - tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_19")]; - 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(70)]; - 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_18")]; - tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_17")]; - 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(70)]; - 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(36224)))]; - 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_16")]; - 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(40768)))]; - 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(45312)))]; - 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(49856)))]; - 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(50112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62464))))[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(63552)))]; - tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_15")]; - 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_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_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109248))))[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_14")]; - tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; - tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; - int32 var_150_axis_0 = const()[name = string("op_150_axis_0"), val = int32(0)]; - int32 var_150_batch_dims_0 = const()[name = string("op_150_batch_dims_0"), val = int32(0)]; - bool var_150_validate_indices_0 = const()[name = string("op_150_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(109504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154624))))[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_13")]; - tensor var_150_cast_fp16_cast_uint16 = gather(axis = var_150_axis_0, batch_dims = var_150_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_150_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_150_cast_fp16_cast_uint16")]; - int32 var_155_axis_0 = const()[name = string("op_155_axis_0"), val = int32(0)]; - int32 var_155_batch_dims_0 = const()[name = string("op_155_batch_dims_0"), val = int32(0)]; - bool var_155_validate_indices_0 = const()[name = string("op_155_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(154880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187712))))[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_12")]; - tensor var_155_cast_fp16_cast_uint16 = gather(axis = var_155_axis_0, batch_dims = var_155_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_155_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_155_cast_fp16_cast_uint16")]; - tensor hidden_7_cast_fp16 = add(x = var_150_cast_fp16_cast_uint16, y = var_155_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_10")]; - 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_11")]; - 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(187904)))]; - 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(190016)))]; - 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_166_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_166_cast_fp16")]; - tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_166_cast_fp16)[name = string("hidden_11_cast_fp16")]; - int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(0)]; - int32 var_170_batch_dims_0 = const()[name = string("op_170_batch_dims_0"), val = int32(0)]; - bool var_170_validate_indices_0 = const()[name = string("op_170_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(191104)))]; - 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_9")]; - tensor var_170_cast_fp16_cast_uint16 = gather(axis = var_170_axis_0, batch_dims = var_170_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_170_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_170_cast_fp16_cast_uint16")]; - tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_170_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; - tensor op_174_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59239232))), scale = tensor([[[0x1.21p-5]]]))[name = string("op_174_to_fp16_quantized")]; - tensor hidden_17_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_174_to_fp16_quantized)[name = string("hidden_17_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(261888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267072))))[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(267200)))]; - 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_186_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_186_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_58, interleave = input_15_interleave_0, values = (var_186_cast_fp16, present_to_fp16))[name = string("input_15_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(274432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279616))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; - 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(280704)))]; - tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; - tensor var_204_axes_0 = const()[name = string("op_204_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_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = time_to_fp16)[name = string("op_204_cast_fp16")]; - tensor var_205_to_fp16 = const()[name = string("op_205_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281792)))]; - tensor phases_1_cast_fp16 = mul(x = var_204_cast_fp16, y = var_205_to_fp16)[name = string("phases_1_cast_fp16")]; - fp16 var_207_to_fp16 = const()[name = string("op_207_to_fp16"), val = fp16(0x1.92p+2)]; - tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_207_to_fp16)[name = string("phases_cast_fp16")]; - tensor var_209_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_209_cast_fp16")]; - tensor var_210_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_210_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_58, interleave = input_21_interleave_0, values = (var_209_cast_fp16, var_210_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(282368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(544576))))[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(545664)))]; - 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(546752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808960))))[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(810048)))]; - 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_220_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_220_cast_fp16")]; - tensor var_221_cast_fp16 = add(x = var_220_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_221_cast_fp16")]; - tensor var_222_cast_fp16 = add(x = var_221_cast_fp16, y = linear_2_cast_fp16)[name = string("op_222_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(811136)))]; - tensor cond_1_cast_fp16 = add(x = var_222_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; - fp16 var_61_to_fp16 = const()[name = string("op_61_to_fp16"), val = fp16(0x0p+0)]; - fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x1p+0)]; - tensor clip_0_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; - tensor clip_1_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; - tensor var_236_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_236_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(812224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(828672))))[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(829760)))]; - 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_236_cast_fp16)[name = string("linear_5_cast_fp16")]; - tensor var_242_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_242_cast_fp16")]; - tensor var_243_axes_0 = const()[name = string("op_243_axes_0"), val = tensor([-1])]; - tensor var_243_cast_fp16 = expand_dims(axes = var_243_axes_0, x = clip_1_cast_fp16)[name = string("op_243_cast_fp16")]; - tensor var_244_cast_fp16 = mul(x = var_242_cast_fp16, y = var_243_cast_fp16)[name = string("op_244_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(830848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(847296))))[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_244_cast_fp16)[name = string("linear_6_cast_fp16")]; - tensor var_250_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_250_cast_fp16")]; - tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_250_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_260_axes_0 = const()[name = string("op_260_axes_0"), val = tensor([-1])]; - tensor var_260_cast_fp16 = expand_dims(axes = var_260_axes_0, x = mask_7_cast_fp16)[name = string("op_260_cast_fp16")]; - tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_260_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(848384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110592))))[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(1111680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1373888))))[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_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_37_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_37_cast_fp16")]; - tensor input_33_perm_0 = const()[name = string("input_33_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_33_cast_fp16 = transpose(perm = input_33_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; - tensor linear_9_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_9_cast_fp16")]; - tensor op_277_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1374976))), scale = tensor([[[0x1.278p-5]]]))[name = string("op_277_to_fp16_quantized")]; - tensor context_3_cast_fp16 = add(x = linear_9_cast_fp16, y = op_277_to_fp16_quantized)[name = string("context_3_cast_fp16")]; - tensor var_280_to_fp16 = const()[name = string("op_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1424192)))]; - tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_280_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_73, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_17_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_73, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; - fp16 var_288_to_fp16 = const()[name = string("op_288_to_fp16"), val = fp16(0x1p-3)]; - tensor input_41_cast_fp16 = mul(x = x_1_cast_fp16, y = var_288_to_fp16)[name = string("input_41_cast_fp16")]; - tensor input_39_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("input_39_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(1425280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2998208))))[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(3004416)))]; - tensor linear_10_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_39_cast_fp16)[name = string("linear_10_cast_fp16")]; - tensor var_322_split_sizes_0 = const()[name = string("op_322_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_322_axis_0 = const()[name = string("op_322_axis_0"), val = int32(-1)]; - tensor var_322_cast_fp16_0, tensor var_322_cast_fp16_1, tensor var_322_cast_fp16_2, tensor var_322_cast_fp16_3, tensor var_322_cast_fp16_4, tensor var_322_cast_fp16_5 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = linear_10_cast_fp16)[name = string("op_322_cast_fp16")]; - tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; - tensor var_328_cast_fp16 = expand_dims(axes = var_328_axes_0, x = var_322_cast_fp16_2)[name = string("op_328_cast_fp16")]; - tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; - fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x1.8p-23)]; - tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_69_to_fp16, x = input_41_cast_fp16)[name = string("x_3_cast_fp16")]; - tensor var_331_axes_0 = const()[name = string("op_331_axes_0"), val = tensor([1])]; - tensor var_331_cast_fp16 = expand_dims(axes = var_331_axes_0, x = var_322_cast_fp16_1)[name = string("op_331_cast_fp16")]; - fp16 var_332_to_fp16 = const()[name = string("op_332_to_fp16"), val = fp16(0x1p+0)]; - tensor var_333_cast_fp16 = add(x = var_331_cast_fp16, y = var_332_to_fp16)[name = string("op_333_cast_fp16")]; - tensor var_334_cast_fp16 = mul(x = x_3_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_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_322_cast_fp16_0)[name = string("op_335_cast_fp16")]; - tensor x_5_cast_fp16 = add(x = var_334_cast_fp16, y = var_335_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(3010624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3797120))))[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(3800256)))]; - tensor linear_11_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_11_cast_fp16")]; - tensor var_347 = const()[name = string("op_347"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_1_cast_fp16 = reshape(shape = var_347, x = linear_11_cast_fp16)[name = string("qkv_1_cast_fp16")]; - tensor var_349_split_sizes_0 = const()[name = string("op_349_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_349_axis_0 = const()[name = string("op_349_axis_0"), val = int32(2)]; - tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1, tensor var_349_cast_fp16_2 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_349_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_349_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_349_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_349_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; - tensor var_354_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_354_cast_fp16")]; - tensor var_356_axes_0 = const()[name = string("op_356_axes_0"), val = tensor([-1])]; - bool var_356_keep_dims_0 = const()[name = string("op_356_keep_dims_0"), val = bool(true)]; - tensor var_356_cast_fp16 = reduce_mean(axes = var_356_axes_0, keep_dims = var_356_keep_dims_0, x = var_354_cast_fp16)[name = string("op_356_cast_fp16")]; - fp16 var_357_to_fp16 = const()[name = string("op_357_to_fp16"), val = fp16(0x1p-23)]; - tensor var_358_cast_fp16 = add(x = var_356_cast_fp16, y = var_357_to_fp16)[name = string("op_358_cast_fp16")]; - fp32 var_359_epsilon_0 = const()[name = string("op_359_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_359_cast_fp16 = rsqrt(epsilon = var_359_epsilon_0, x = var_358_cast_fp16)[name = string("op_359_cast_fp16")]; - tensor var_360_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_359_cast_fp16)[name = string("op_360_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(3803392)))]; - tensor var_361_cast_fp16 = mul(x = var_360_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_361_cast_fp16")]; - tensor var_364_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_364_cast_fp16")]; - tensor var_366_axes_0 = const()[name = string("op_366_axes_0"), val = tensor([-1])]; - bool var_366_keep_dims_0 = const()[name = string("op_366_keep_dims_0"), val = bool(true)]; - tensor var_366_cast_fp16 = reduce_mean(axes = var_366_axes_0, keep_dims = var_366_keep_dims_0, x = var_364_cast_fp16)[name = string("op_366_cast_fp16")]; - fp16 var_367_to_fp16 = const()[name = string("op_367_to_fp16"), val = fp16(0x1p-23)]; - tensor var_368_cast_fp16 = add(x = var_366_cast_fp16, y = var_367_to_fp16)[name = string("op_368_cast_fp16")]; - fp32 var_369_epsilon_0 = const()[name = string("op_369_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_369_cast_fp16 = rsqrt(epsilon = var_369_epsilon_0, x = var_368_cast_fp16)[name = string("op_369_cast_fp16")]; - tensor var_370_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_369_cast_fp16)[name = string("op_370_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(3803584)))]; - tensor var_371_cast_fp16 = mul(x = var_370_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_371_cast_fp16")]; - tensor var_376 = const()[name = string("op_376"), val = tensor([1, 1, 1, 352])]; - tensor var_377 = reshape(shape = var_376, x = valid_mask)[name = string("op_377")]; - tensor var_379 = logical_not(x = var_377)[name = string("op_379")]; - fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(-inf)]; - tensor var_375_to_fp16 = const()[name = string("op_375_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59370368)))]; - tensor attn_mask_1_cast_fp16 = select(a = var_68_to_fp16, b = var_375_to_fp16, cond = var_379)[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_371_cast_fp16)[name = string("transpose_155")]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_361_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_382_perm_0 = const()[name = string("op_382_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_383 = const()[name = string("op_383"), val = tensor([1, 352, 512])]; - tensor var_382_cast_fp16 = transpose(perm = var_382_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; - tensor input_43_cast_fp16 = reshape(shape = var_383, x = var_382_cast_fp16)[name = string("input_43_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(3804288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4066496))))[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(4067584)))]; - tensor linear_12_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_43_cast_fp16)[name = string("linear_12_cast_fp16")]; - tensor var_389_cast_fp16 = mul(x = var_328_cast_fp16, y = linear_12_cast_fp16)[name = string("op_389_cast_fp16")]; - tensor input_47_cast_fp16 = add(x = input_41_cast_fp16, y = var_389_cast_fp16)[name = string("input_47_cast_fp16")]; - tensor var_391_axes_0 = const()[name = string("op_391_axes_0"), val = tensor([1])]; - tensor var_391_cast_fp16 = expand_dims(axes = var_391_axes_0, x = var_322_cast_fp16_5)[name = string("op_391_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_69_to_fp16, x = input_47_cast_fp16)[name = string("x_7_cast_fp16")]; - tensor var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor([1])]; - tensor var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_322_cast_fp16_4)[name = string("op_394_cast_fp16")]; - fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p+0)]; - tensor var_396_cast_fp16 = add(x = var_394_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; - tensor var_397_cast_fp16 = mul(x = x_7_cast_fp16, y = var_396_cast_fp16)[name = string("op_397_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_322_cast_fp16_3)[name = string("op_398_cast_fp16")]; - tensor input_49_cast_fp16 = add(x = var_397_cast_fp16, y = var_398_cast_fp16)[name = string("input_49_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(4068672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5117312))))[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(5121472)))]; - tensor linear_13_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_49_cast_fp16)[name = string("linear_13_cast_fp16")]; - string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; - tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_13_cast_fp16)[name = string("input_53_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(5125632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6174272))))[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(6175360)))]; - tensor linear_14_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_53_cast_fp16)[name = string("linear_14_cast_fp16")]; - tensor var_411_cast_fp16 = mul(x = var_391_cast_fp16, y = linear_14_cast_fp16)[name = string("op_411_cast_fp16")]; - tensor input_61_cast_fp16 = add(x = input_47_cast_fp16, y = var_411_cast_fp16)[name = string("input_61_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(6176448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7749376))))[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(7755584)))]; - tensor linear_15_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_39_cast_fp16)[name = string("linear_15_cast_fp16")]; - tensor var_420_split_sizes_0 = const()[name = string("op_420_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_420_axis_0 = const()[name = string("op_420_axis_0"), val = int32(-1)]; - tensor var_420_cast_fp16_0, tensor var_420_cast_fp16_1, tensor var_420_cast_fp16_2, tensor var_420_cast_fp16_3, tensor var_420_cast_fp16_4, tensor var_420_cast_fp16_5 = split(axis = var_420_axis_0, split_sizes = var_420_split_sizes_0, x = linear_15_cast_fp16)[name = string("op_420_cast_fp16")]; - tensor var_426_axes_0 = const()[name = string("op_426_axes_0"), val = tensor([1])]; - tensor var_426_cast_fp16 = expand_dims(axes = var_426_axes_0, x = var_420_cast_fp16_2)[name = string("op_426_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_69_to_fp16, x = input_61_cast_fp16)[name = string("x_9_cast_fp16")]; - tensor var_429_axes_0 = const()[name = string("op_429_axes_0"), val = tensor([1])]; - tensor var_429_cast_fp16 = expand_dims(axes = var_429_axes_0, x = var_420_cast_fp16_1)[name = string("op_429_cast_fp16")]; - fp16 var_430_to_fp16 = const()[name = string("op_430_to_fp16"), val = fp16(0x1p+0)]; - tensor var_431_cast_fp16 = add(x = var_429_cast_fp16, y = var_430_to_fp16)[name = string("op_431_cast_fp16")]; - tensor var_432_cast_fp16 = mul(x = x_9_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_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_420_cast_fp16_0)[name = string("op_433_cast_fp16")]; - tensor x_11_cast_fp16 = add(x = var_432_cast_fp16, y = var_433_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(7761792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548288))))[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(8551424)))]; - tensor linear_16_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_16_cast_fp16")]; - tensor var_445 = const()[name = string("op_445"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_3_cast_fp16 = reshape(shape = var_445, x = linear_16_cast_fp16)[name = string("qkv_3_cast_fp16")]; - tensor var_447_split_sizes_0 = const()[name = string("op_447_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_447_axis_0 = const()[name = string("op_447_axis_0"), val = int32(2)]; - tensor var_447_cast_fp16_0, tensor var_447_cast_fp16_1, tensor var_447_cast_fp16_2 = split(axis = var_447_axis_0, split_sizes = var_447_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_447_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_447_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_447_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_447_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; - tensor var_452_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_452_cast_fp16")]; - tensor var_454_axes_0 = const()[name = string("op_454_axes_0"), val = tensor([-1])]; - bool var_454_keep_dims_0 = const()[name = string("op_454_keep_dims_0"), val = bool(true)]; - tensor var_454_cast_fp16 = reduce_mean(axes = var_454_axes_0, keep_dims = var_454_keep_dims_0, x = var_452_cast_fp16)[name = string("op_454_cast_fp16")]; - fp16 var_455_to_fp16 = const()[name = string("op_455_to_fp16"), val = fp16(0x1p-23)]; - tensor var_456_cast_fp16 = add(x = var_454_cast_fp16, y = var_455_to_fp16)[name = string("op_456_cast_fp16")]; - fp32 var_457_epsilon_0 = const()[name = string("op_457_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_457_cast_fp16 = rsqrt(epsilon = var_457_epsilon_0, x = var_456_cast_fp16)[name = string("op_457_cast_fp16")]; - tensor var_458_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_457_cast_fp16)[name = string("op_458_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(8554560)))]; - tensor var_459_cast_fp16 = mul(x = var_458_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_459_cast_fp16")]; - tensor var_462_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_462_cast_fp16")]; - tensor var_464_axes_0 = const()[name = string("op_464_axes_0"), val = tensor([-1])]; - bool var_464_keep_dims_0 = const()[name = string("op_464_keep_dims_0"), val = bool(true)]; - tensor var_464_cast_fp16 = reduce_mean(axes = var_464_axes_0, keep_dims = var_464_keep_dims_0, x = var_462_cast_fp16)[name = string("op_464_cast_fp16")]; - fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1p-23)]; - tensor var_466_cast_fp16 = add(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("op_466_cast_fp16")]; - fp32 var_467_epsilon_0 = const()[name = string("op_467_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_467_cast_fp16 = rsqrt(epsilon = var_467_epsilon_0, x = var_466_cast_fp16)[name = string("op_467_cast_fp16")]; - tensor var_468_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_467_cast_fp16)[name = string("op_468_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(8554752)))]; - tensor var_469_cast_fp16 = mul(x = var_468_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_469_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_469_cast_fp16)[name = string("transpose_151")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_459_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_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, 352, 512])]; - tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; - tensor input_63_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("input_63_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(8554944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8817152))))[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(8818240)))]; - tensor linear_17_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_63_cast_fp16)[name = string("linear_17_cast_fp16")]; - tensor var_487_cast_fp16 = mul(x = var_426_cast_fp16, y = linear_17_cast_fp16)[name = string("op_487_cast_fp16")]; - tensor input_67_cast_fp16 = add(x = input_61_cast_fp16, y = var_487_cast_fp16)[name = string("input_67_cast_fp16")]; - tensor var_489_axes_0 = const()[name = string("op_489_axes_0"), val = tensor([1])]; - tensor var_489_cast_fp16 = expand_dims(axes = var_489_axes_0, x = var_420_cast_fp16_5)[name = string("op_489_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_69_to_fp16, x = input_67_cast_fp16)[name = string("x_13_cast_fp16")]; - tensor var_492_axes_0 = const()[name = string("op_492_axes_0"), val = tensor([1])]; - tensor var_492_cast_fp16 = expand_dims(axes = var_492_axes_0, x = var_420_cast_fp16_4)[name = string("op_492_cast_fp16")]; - fp16 var_493_to_fp16 = const()[name = string("op_493_to_fp16"), val = fp16(0x1p+0)]; - tensor var_494_cast_fp16 = add(x = var_492_cast_fp16, y = var_493_to_fp16)[name = string("op_494_cast_fp16")]; - tensor var_495_cast_fp16 = mul(x = x_13_cast_fp16, y = var_494_cast_fp16)[name = string("op_495_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_420_cast_fp16_3)[name = string("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = var_495_cast_fp16, y = var_496_cast_fp16)[name = string("input_69_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(8819328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867968))))[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(9872128)))]; - tensor linear_18_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_69_cast_fp16)[name = string("linear_18_cast_fp16")]; - string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")]; - tensor input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = linear_18_cast_fp16)[name = string("input_73_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(9876288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10924928))))[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(10926016)))]; - tensor linear_19_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_73_cast_fp16)[name = string("linear_19_cast_fp16")]; - tensor var_509_cast_fp16 = mul(x = var_489_cast_fp16, y = linear_19_cast_fp16)[name = string("op_509_cast_fp16")]; - tensor input_81_cast_fp16 = add(x = input_67_cast_fp16, y = var_509_cast_fp16)[name = string("input_81_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(10927104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12500032))))[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(12506240)))]; - tensor linear_20_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_39_cast_fp16)[name = string("linear_20_cast_fp16")]; - tensor var_518_split_sizes_0 = const()[name = string("op_518_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_518_axis_0 = const()[name = string("op_518_axis_0"), val = int32(-1)]; - tensor var_518_cast_fp16_0, tensor var_518_cast_fp16_1, tensor var_518_cast_fp16_2, tensor var_518_cast_fp16_3, tensor var_518_cast_fp16_4, tensor var_518_cast_fp16_5 = split(axis = var_518_axis_0, split_sizes = var_518_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_518_cast_fp16")]; - tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; - tensor var_524_cast_fp16 = expand_dims(axes = var_524_axes_0, x = var_518_cast_fp16_2)[name = string("op_524_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_69_to_fp16, x = input_81_cast_fp16)[name = string("x_15_cast_fp16")]; - tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([1])]; - tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = var_518_cast_fp16_1)[name = string("op_527_cast_fp16")]; - fp16 var_528_to_fp16 = const()[name = string("op_528_to_fp16"), val = fp16(0x1p+0)]; - tensor var_529_cast_fp16 = add(x = var_527_cast_fp16, y = var_528_to_fp16)[name = string("op_529_cast_fp16")]; - tensor var_530_cast_fp16 = mul(x = x_15_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_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_518_cast_fp16_0)[name = string("op_531_cast_fp16")]; - tensor x_17_cast_fp16 = add(x = var_530_cast_fp16, y = var_531_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(12512448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13298944))))[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(13302080)))]; - tensor linear_21_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_21_cast_fp16")]; - tensor var_543 = const()[name = string("op_543"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_5_cast_fp16 = reshape(shape = var_543, x = linear_21_cast_fp16)[name = string("qkv_5_cast_fp16")]; - tensor var_545_split_sizes_0 = const()[name = string("op_545_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_545_axis_0 = const()[name = string("op_545_axis_0"), val = int32(2)]; - tensor var_545_cast_fp16_0, tensor var_545_cast_fp16_1, tensor var_545_cast_fp16_2 = split(axis = var_545_axis_0, split_sizes = var_545_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_545_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_545_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_545_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_545_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; - tensor var_550_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_550_cast_fp16")]; - tensor var_552_axes_0 = const()[name = string("op_552_axes_0"), val = tensor([-1])]; - bool var_552_keep_dims_0 = const()[name = string("op_552_keep_dims_0"), val = bool(true)]; - tensor var_552_cast_fp16 = reduce_mean(axes = var_552_axes_0, keep_dims = var_552_keep_dims_0, x = var_550_cast_fp16)[name = string("op_552_cast_fp16")]; - fp16 var_553_to_fp16 = const()[name = string("op_553_to_fp16"), val = fp16(0x1p-23)]; - tensor var_554_cast_fp16 = add(x = var_552_cast_fp16, y = var_553_to_fp16)[name = string("op_554_cast_fp16")]; - fp32 var_555_epsilon_0 = const()[name = string("op_555_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_555_cast_fp16 = rsqrt(epsilon = var_555_epsilon_0, x = var_554_cast_fp16)[name = string("op_555_cast_fp16")]; - tensor var_556_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_555_cast_fp16)[name = string("op_556_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(13305216)))]; - tensor var_557_cast_fp16 = mul(x = var_556_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_557_cast_fp16")]; - tensor var_560_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_560_cast_fp16")]; - tensor var_562_axes_0 = const()[name = string("op_562_axes_0"), val = tensor([-1])]; - bool var_562_keep_dims_0 = const()[name = string("op_562_keep_dims_0"), val = bool(true)]; - tensor var_562_cast_fp16 = reduce_mean(axes = var_562_axes_0, keep_dims = var_562_keep_dims_0, x = var_560_cast_fp16)[name = string("op_562_cast_fp16")]; - fp16 var_563_to_fp16 = const()[name = string("op_563_to_fp16"), val = fp16(0x1p-23)]; - tensor var_564_cast_fp16 = add(x = var_562_cast_fp16, y = var_563_to_fp16)[name = string("op_564_cast_fp16")]; - fp32 var_565_epsilon_0 = const()[name = string("op_565_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_565_cast_fp16 = rsqrt(epsilon = var_565_epsilon_0, x = var_564_cast_fp16)[name = string("op_565_cast_fp16")]; - tensor var_566_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_565_cast_fp16)[name = string("op_566_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(13305408)))]; - tensor var_567_cast_fp16 = mul(x = var_566_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_567_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_567_cast_fp16)[name = string("transpose_147")]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_557_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_578_perm_0 = const()[name = string("op_578_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_579 = const()[name = string("op_579"), val = tensor([1, 352, 512])]; - tensor var_578_cast_fp16 = transpose(perm = var_578_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; - tensor input_83_cast_fp16 = reshape(shape = var_579, x = var_578_cast_fp16)[name = string("input_83_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(13305600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13567808))))[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(13568896)))]; - tensor linear_22_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_83_cast_fp16)[name = string("linear_22_cast_fp16")]; - tensor var_585_cast_fp16 = mul(x = var_524_cast_fp16, y = linear_22_cast_fp16)[name = string("op_585_cast_fp16")]; - tensor input_87_cast_fp16 = add(x = input_81_cast_fp16, y = var_585_cast_fp16)[name = string("input_87_cast_fp16")]; - tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([1])]; - tensor var_587_cast_fp16 = expand_dims(axes = var_587_axes_0, x = var_518_cast_fp16_5)[name = string("op_587_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_69_to_fp16, x = input_87_cast_fp16)[name = string("x_19_cast_fp16")]; - tensor var_590_axes_0 = const()[name = string("op_590_axes_0"), val = tensor([1])]; - tensor var_590_cast_fp16 = expand_dims(axes = var_590_axes_0, x = var_518_cast_fp16_4)[name = string("op_590_cast_fp16")]; - fp16 var_591_to_fp16 = const()[name = string("op_591_to_fp16"), val = fp16(0x1p+0)]; - tensor var_592_cast_fp16 = add(x = var_590_cast_fp16, y = var_591_to_fp16)[name = string("op_592_cast_fp16")]; - tensor var_593_cast_fp16 = mul(x = x_19_cast_fp16, y = var_592_cast_fp16)[name = string("op_593_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_518_cast_fp16_3)[name = string("op_594_cast_fp16")]; - tensor input_89_cast_fp16 = add(x = var_593_cast_fp16, y = var_594_cast_fp16)[name = string("input_89_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(13569984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14618624))))[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(14622784)))]; - tensor linear_23_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_89_cast_fp16)[name = string("linear_23_cast_fp16")]; - string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")]; - tensor input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = linear_23_cast_fp16)[name = string("input_93_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(14626944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15675584))))[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(15676672)))]; - tensor linear_24_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_93_cast_fp16)[name = string("linear_24_cast_fp16")]; - tensor var_607_cast_fp16 = mul(x = var_587_cast_fp16, y = linear_24_cast_fp16)[name = string("op_607_cast_fp16")]; - tensor input_101_cast_fp16 = add(x = input_87_cast_fp16, y = var_607_cast_fp16)[name = string("input_101_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(15677760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17250688))))[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(17256896)))]; - tensor linear_25_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_39_cast_fp16)[name = string("linear_25_cast_fp16")]; - tensor var_616_split_sizes_0 = const()[name = string("op_616_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_616_axis_0 = const()[name = string("op_616_axis_0"), val = int32(-1)]; - tensor var_616_cast_fp16_0, tensor var_616_cast_fp16_1, tensor var_616_cast_fp16_2, tensor var_616_cast_fp16_3, tensor var_616_cast_fp16_4, tensor var_616_cast_fp16_5 = split(axis = var_616_axis_0, split_sizes = var_616_split_sizes_0, x = linear_25_cast_fp16)[name = string("op_616_cast_fp16")]; - tensor var_622_axes_0 = const()[name = string("op_622_axes_0"), val = tensor([1])]; - tensor var_622_cast_fp16 = expand_dims(axes = var_622_axes_0, x = var_616_cast_fp16_2)[name = string("op_622_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_69_to_fp16, x = input_101_cast_fp16)[name = string("x_21_cast_fp16")]; - tensor var_625_axes_0 = const()[name = string("op_625_axes_0"), val = tensor([1])]; - tensor var_625_cast_fp16 = expand_dims(axes = var_625_axes_0, x = var_616_cast_fp16_1)[name = string("op_625_cast_fp16")]; - fp16 var_626_to_fp16 = const()[name = string("op_626_to_fp16"), val = fp16(0x1p+0)]; - tensor var_627_cast_fp16 = add(x = var_625_cast_fp16, y = var_626_to_fp16)[name = string("op_627_cast_fp16")]; - tensor var_628_cast_fp16 = mul(x = x_21_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_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_616_cast_fp16_0)[name = string("op_629_cast_fp16")]; - tensor x_23_cast_fp16 = add(x = var_628_cast_fp16, y = var_629_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(17263104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18049600))))[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(18052736)))]; - tensor linear_26_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_26_cast_fp16")]; - tensor var_641 = const()[name = string("op_641"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_7_cast_fp16 = reshape(shape = var_641, x = linear_26_cast_fp16)[name = string("qkv_7_cast_fp16")]; - tensor var_643_split_sizes_0 = const()[name = string("op_643_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_643_axis_0 = const()[name = string("op_643_axis_0"), val = int32(2)]; - tensor var_643_cast_fp16_0, tensor var_643_cast_fp16_1, tensor var_643_cast_fp16_2 = split(axis = var_643_axis_0, split_sizes = var_643_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_643_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_643_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_643_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_643_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; - tensor var_648_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_648_cast_fp16")]; - tensor var_650_axes_0 = const()[name = string("op_650_axes_0"), val = tensor([-1])]; - bool var_650_keep_dims_0 = const()[name = string("op_650_keep_dims_0"), val = bool(true)]; - tensor var_650_cast_fp16 = reduce_mean(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = string("op_650_cast_fp16")]; - fp16 var_651_to_fp16 = const()[name = string("op_651_to_fp16"), val = fp16(0x1p-23)]; - tensor var_652_cast_fp16 = add(x = var_650_cast_fp16, y = var_651_to_fp16)[name = string("op_652_cast_fp16")]; - fp32 var_653_epsilon_0 = const()[name = string("op_653_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_653_cast_fp16 = rsqrt(epsilon = var_653_epsilon_0, x = var_652_cast_fp16)[name = string("op_653_cast_fp16")]; - tensor var_654_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_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(18055872)))]; - tensor var_655_cast_fp16 = mul(x = var_654_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_655_cast_fp16")]; - tensor var_658_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_658_cast_fp16")]; - tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; - bool var_660_keep_dims_0 = const()[name = string("op_660_keep_dims_0"), val = bool(true)]; - tensor var_660_cast_fp16 = reduce_mean(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = string("op_660_cast_fp16")]; - fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-23)]; - tensor var_662_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("op_662_cast_fp16")]; - fp32 var_663_epsilon_0 = const()[name = string("op_663_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_663_cast_fp16 = rsqrt(epsilon = var_663_epsilon_0, x = var_662_cast_fp16)[name = string("op_663_cast_fp16")]; - tensor var_664_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_663_cast_fp16)[name = string("op_664_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(18056064)))]; - tensor var_665_cast_fp16 = mul(x = var_664_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_665_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_665_cast_fp16)[name = string("transpose_143")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_655_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_676_perm_0 = const()[name = string("op_676_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_677 = const()[name = string("op_677"), val = tensor([1, 352, 512])]; - tensor var_676_cast_fp16 = transpose(perm = var_676_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; - tensor input_103_cast_fp16 = reshape(shape = var_677, x = var_676_cast_fp16)[name = string("input_103_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(18056256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18318464))))[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(18319552)))]; - tensor linear_27_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_103_cast_fp16)[name = string("linear_27_cast_fp16")]; - tensor var_683_cast_fp16 = mul(x = var_622_cast_fp16, y = linear_27_cast_fp16)[name = string("op_683_cast_fp16")]; - tensor input_107_cast_fp16 = add(x = input_101_cast_fp16, y = var_683_cast_fp16)[name = string("input_107_cast_fp16")]; - tensor var_685_axes_0 = const()[name = string("op_685_axes_0"), val = tensor([1])]; - tensor var_685_cast_fp16 = expand_dims(axes = var_685_axes_0, x = var_616_cast_fp16_5)[name = string("op_685_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_69_to_fp16, x = input_107_cast_fp16)[name = string("x_25_cast_fp16")]; - tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([1])]; - tensor var_688_cast_fp16 = expand_dims(axes = var_688_axes_0, x = var_616_cast_fp16_4)[name = string("op_688_cast_fp16")]; - fp16 var_689_to_fp16 = const()[name = string("op_689_to_fp16"), val = fp16(0x1p+0)]; - tensor var_690_cast_fp16 = add(x = var_688_cast_fp16, y = var_689_to_fp16)[name = string("op_690_cast_fp16")]; - tensor var_691_cast_fp16 = mul(x = x_25_cast_fp16, y = var_690_cast_fp16)[name = string("op_691_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_616_cast_fp16_3)[name = string("op_692_cast_fp16")]; - tensor input_109_cast_fp16 = add(x = var_691_cast_fp16, y = var_692_cast_fp16)[name = string("input_109_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(18320640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19369280))))[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(19373440)))]; - tensor linear_28_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_109_cast_fp16)[name = string("linear_28_cast_fp16")]; - string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; - tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_28_cast_fp16)[name = string("input_113_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(19377600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20426240))))[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(20427328)))]; - tensor linear_29_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_113_cast_fp16)[name = string("linear_29_cast_fp16")]; - tensor var_705_cast_fp16 = mul(x = var_685_cast_fp16, y = linear_29_cast_fp16)[name = string("op_705_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_107_cast_fp16, y = var_705_cast_fp16)[name = string("input_121_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(20428416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001344))))[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(22007552)))]; - tensor linear_30_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_39_cast_fp16)[name = string("linear_30_cast_fp16")]; - tensor var_714_split_sizes_0 = const()[name = string("op_714_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_714_axis_0 = const()[name = string("op_714_axis_0"), val = int32(-1)]; - tensor var_714_cast_fp16_0, tensor var_714_cast_fp16_1, tensor var_714_cast_fp16_2, tensor var_714_cast_fp16_3, tensor var_714_cast_fp16_4, tensor var_714_cast_fp16_5 = split(axis = var_714_axis_0, split_sizes = var_714_split_sizes_0, x = linear_30_cast_fp16)[name = string("op_714_cast_fp16")]; - tensor var_720_axes_0 = const()[name = string("op_720_axes_0"), val = tensor([1])]; - tensor var_720_cast_fp16 = expand_dims(axes = var_720_axes_0, x = var_714_cast_fp16_2)[name = string("op_720_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_69_to_fp16, x = input_121_cast_fp16)[name = string("x_27_cast_fp16")]; - tensor var_723_axes_0 = const()[name = string("op_723_axes_0"), val = tensor([1])]; - tensor var_723_cast_fp16 = expand_dims(axes = var_723_axes_0, x = var_714_cast_fp16_1)[name = string("op_723_cast_fp16")]; - fp16 var_724_to_fp16 = const()[name = string("op_724_to_fp16"), val = fp16(0x1p+0)]; - tensor var_725_cast_fp16 = add(x = var_723_cast_fp16, y = var_724_to_fp16)[name = string("op_725_cast_fp16")]; - tensor var_726_cast_fp16 = mul(x = x_27_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_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_714_cast_fp16_0)[name = string("op_727_cast_fp16")]; - tensor x_29_cast_fp16 = add(x = var_726_cast_fp16, y = var_727_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(22013760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22800256))))[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(22803392)))]; - tensor linear_31_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_31_cast_fp16")]; - tensor var_739 = const()[name = string("op_739"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_9_cast_fp16 = reshape(shape = var_739, x = linear_31_cast_fp16)[name = string("qkv_9_cast_fp16")]; - tensor var_741_split_sizes_0 = const()[name = string("op_741_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_741_axis_0 = const()[name = string("op_741_axis_0"), val = int32(2)]; - tensor var_741_cast_fp16_0, tensor var_741_cast_fp16_1, tensor var_741_cast_fp16_2 = split(axis = var_741_axis_0, split_sizes = var_741_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_741_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_741_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_741_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_741_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; - tensor var_746_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_746_cast_fp16")]; - tensor var_748_axes_0 = const()[name = string("op_748_axes_0"), val = tensor([-1])]; - bool var_748_keep_dims_0 = const()[name = string("op_748_keep_dims_0"), val = bool(true)]; - tensor var_748_cast_fp16 = reduce_mean(axes = var_748_axes_0, keep_dims = var_748_keep_dims_0, x = var_746_cast_fp16)[name = string("op_748_cast_fp16")]; - fp16 var_749_to_fp16 = const()[name = string("op_749_to_fp16"), val = fp16(0x1p-23)]; - tensor var_750_cast_fp16 = add(x = var_748_cast_fp16, y = var_749_to_fp16)[name = string("op_750_cast_fp16")]; - fp32 var_751_epsilon_0 = const()[name = string("op_751_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_751_cast_fp16 = rsqrt(epsilon = var_751_epsilon_0, x = var_750_cast_fp16)[name = string("op_751_cast_fp16")]; - tensor var_752_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_751_cast_fp16)[name = string("op_752_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(22806528)))]; - tensor var_753_cast_fp16 = mul(x = var_752_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_753_cast_fp16")]; - tensor var_756_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_756_cast_fp16")]; - tensor var_758_axes_0 = const()[name = string("op_758_axes_0"), val = tensor([-1])]; - bool var_758_keep_dims_0 = const()[name = string("op_758_keep_dims_0"), val = bool(true)]; - tensor var_758_cast_fp16 = reduce_mean(axes = var_758_axes_0, keep_dims = var_758_keep_dims_0, x = var_756_cast_fp16)[name = string("op_758_cast_fp16")]; - fp16 var_759_to_fp16 = const()[name = string("op_759_to_fp16"), val = fp16(0x1p-23)]; - tensor var_760_cast_fp16 = add(x = var_758_cast_fp16, y = var_759_to_fp16)[name = string("op_760_cast_fp16")]; - fp32 var_761_epsilon_0 = const()[name = string("op_761_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_761_cast_fp16 = rsqrt(epsilon = var_761_epsilon_0, x = var_760_cast_fp16)[name = string("op_761_cast_fp16")]; - tensor var_762_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_761_cast_fp16)[name = string("op_762_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(22806720)))]; - tensor var_763_cast_fp16 = mul(x = var_762_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_763_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_763_cast_fp16)[name = string("transpose_139")]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_753_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_774_perm_0 = const()[name = string("op_774_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_775 = const()[name = string("op_775"), val = tensor([1, 352, 512])]; - tensor var_774_cast_fp16 = transpose(perm = var_774_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; - tensor input_123_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("input_123_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(22806912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23069120))))[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(23070208)))]; - tensor linear_32_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_123_cast_fp16)[name = string("linear_32_cast_fp16")]; - tensor var_781_cast_fp16 = mul(x = var_720_cast_fp16, y = linear_32_cast_fp16)[name = string("op_781_cast_fp16")]; - tensor input_127_cast_fp16 = add(x = input_121_cast_fp16, y = var_781_cast_fp16)[name = string("input_127_cast_fp16")]; - tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([1])]; - tensor var_783_cast_fp16 = expand_dims(axes = var_783_axes_0, x = var_714_cast_fp16_5)[name = string("op_783_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_69_to_fp16, x = input_127_cast_fp16)[name = string("x_31_cast_fp16")]; - tensor var_786_axes_0 = const()[name = string("op_786_axes_0"), val = tensor([1])]; - tensor var_786_cast_fp16 = expand_dims(axes = var_786_axes_0, x = var_714_cast_fp16_4)[name = string("op_786_cast_fp16")]; - fp16 var_787_to_fp16 = const()[name = string("op_787_to_fp16"), val = fp16(0x1p+0)]; - tensor var_788_cast_fp16 = add(x = var_786_cast_fp16, y = var_787_to_fp16)[name = string("op_788_cast_fp16")]; - tensor var_789_cast_fp16 = mul(x = x_31_cast_fp16, y = var_788_cast_fp16)[name = string("op_789_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_714_cast_fp16_3)[name = string("op_790_cast_fp16")]; - tensor input_129_cast_fp16 = add(x = var_789_cast_fp16, y = var_790_cast_fp16)[name = string("input_129_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(23071296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119936))))[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(24124096)))]; - tensor linear_33_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_129_cast_fp16)[name = string("linear_33_cast_fp16")]; - string input_133_mode_0 = const()[name = string("input_133_mode_0"), val = string("EXACT")]; - tensor input_133_cast_fp16 = gelu(mode = input_133_mode_0, x = linear_33_cast_fp16)[name = string("input_133_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(24128256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176896))))[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(25177984)))]; - tensor linear_34_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_133_cast_fp16)[name = string("linear_34_cast_fp16")]; - tensor var_803_cast_fp16 = mul(x = var_783_cast_fp16, y = linear_34_cast_fp16)[name = string("op_803_cast_fp16")]; - tensor input_141_cast_fp16 = add(x = input_127_cast_fp16, y = var_803_cast_fp16)[name = string("input_141_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(25179072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26752000))))[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(26758208)))]; - tensor linear_35_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_39_cast_fp16)[name = string("linear_35_cast_fp16")]; - tensor var_812_split_sizes_0 = const()[name = string("op_812_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_812_axis_0 = const()[name = string("op_812_axis_0"), val = int32(-1)]; - tensor var_812_cast_fp16_0, tensor var_812_cast_fp16_1, tensor var_812_cast_fp16_2, tensor var_812_cast_fp16_3, tensor var_812_cast_fp16_4, tensor var_812_cast_fp16_5 = split(axis = var_812_axis_0, split_sizes = var_812_split_sizes_0, x = linear_35_cast_fp16)[name = string("op_812_cast_fp16")]; - tensor var_818_axes_0 = const()[name = string("op_818_axes_0"), val = tensor([1])]; - tensor var_818_cast_fp16 = expand_dims(axes = var_818_axes_0, x = var_812_cast_fp16_2)[name = string("op_818_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_69_to_fp16, x = input_141_cast_fp16)[name = string("x_33_cast_fp16")]; - tensor var_821_axes_0 = const()[name = string("op_821_axes_0"), val = tensor([1])]; - tensor var_821_cast_fp16 = expand_dims(axes = var_821_axes_0, x = var_812_cast_fp16_1)[name = string("op_821_cast_fp16")]; - fp16 var_822_to_fp16 = const()[name = string("op_822_to_fp16"), val = fp16(0x1p+0)]; - tensor var_823_cast_fp16 = add(x = var_821_cast_fp16, y = var_822_to_fp16)[name = string("op_823_cast_fp16")]; - tensor var_824_cast_fp16 = mul(x = x_33_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_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_812_cast_fp16_0)[name = string("op_825_cast_fp16")]; - tensor x_35_cast_fp16 = add(x = var_824_cast_fp16, y = var_825_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(26764416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27550912))))[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(27554048)))]; - tensor linear_36_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_36_cast_fp16")]; - tensor var_837 = const()[name = string("op_837"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_11_cast_fp16 = reshape(shape = var_837, x = linear_36_cast_fp16)[name = string("qkv_11_cast_fp16")]; - tensor var_839_split_sizes_0 = const()[name = string("op_839_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_839_axis_0 = const()[name = string("op_839_axis_0"), val = int32(2)]; - tensor var_839_cast_fp16_0, tensor var_839_cast_fp16_1, tensor var_839_cast_fp16_2 = split(axis = var_839_axis_0, split_sizes = var_839_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_839_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_839_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_839_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_839_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; - tensor var_844_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_844_cast_fp16")]; - tensor var_846_axes_0 = const()[name = string("op_846_axes_0"), val = tensor([-1])]; - bool var_846_keep_dims_0 = const()[name = string("op_846_keep_dims_0"), val = bool(true)]; - tensor var_846_cast_fp16 = reduce_mean(axes = var_846_axes_0, keep_dims = var_846_keep_dims_0, x = var_844_cast_fp16)[name = string("op_846_cast_fp16")]; - fp16 var_847_to_fp16 = const()[name = string("op_847_to_fp16"), val = fp16(0x1p-23)]; - tensor var_848_cast_fp16 = add(x = var_846_cast_fp16, y = var_847_to_fp16)[name = string("op_848_cast_fp16")]; - fp32 var_849_epsilon_0 = const()[name = string("op_849_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_849_cast_fp16 = rsqrt(epsilon = var_849_epsilon_0, x = var_848_cast_fp16)[name = string("op_849_cast_fp16")]; - tensor var_850_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_849_cast_fp16)[name = string("op_850_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(27557184)))]; - tensor var_851_cast_fp16 = mul(x = var_850_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_851_cast_fp16")]; - tensor var_854_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_854_cast_fp16")]; - tensor var_856_axes_0 = const()[name = string("op_856_axes_0"), val = tensor([-1])]; - bool var_856_keep_dims_0 = const()[name = string("op_856_keep_dims_0"), val = bool(true)]; - tensor var_856_cast_fp16 = reduce_mean(axes = var_856_axes_0, keep_dims = var_856_keep_dims_0, x = var_854_cast_fp16)[name = string("op_856_cast_fp16")]; - fp16 var_857_to_fp16 = const()[name = string("op_857_to_fp16"), val = fp16(0x1p-23)]; - tensor var_858_cast_fp16 = add(x = var_856_cast_fp16, y = var_857_to_fp16)[name = string("op_858_cast_fp16")]; - fp32 var_859_epsilon_0 = const()[name = string("op_859_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_859_cast_fp16 = rsqrt(epsilon = var_859_epsilon_0, x = var_858_cast_fp16)[name = string("op_859_cast_fp16")]; - tensor var_860_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_859_cast_fp16)[name = string("op_860_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(27557376)))]; - tensor var_861_cast_fp16 = mul(x = var_860_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_861_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_861_cast_fp16)[name = string("transpose_135")]; - tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_851_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_872_perm_0 = const()[name = string("op_872_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_873 = const()[name = string("op_873"), val = tensor([1, 352, 512])]; - tensor var_872_cast_fp16 = transpose(perm = var_872_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; - tensor input_143_cast_fp16 = reshape(shape = var_873, x = var_872_cast_fp16)[name = string("input_143_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(27557568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27819776))))[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(27820864)))]; - tensor linear_37_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_143_cast_fp16)[name = string("linear_37_cast_fp16")]; - tensor var_879_cast_fp16 = mul(x = var_818_cast_fp16, y = linear_37_cast_fp16)[name = string("op_879_cast_fp16")]; - tensor input_147_cast_fp16 = add(x = input_141_cast_fp16, y = var_879_cast_fp16)[name = string("input_147_cast_fp16")]; - tensor var_881_axes_0 = const()[name = string("op_881_axes_0"), val = tensor([1])]; - tensor var_881_cast_fp16 = expand_dims(axes = var_881_axes_0, x = var_812_cast_fp16_5)[name = string("op_881_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_69_to_fp16, x = input_147_cast_fp16)[name = string("x_37_cast_fp16")]; - tensor var_884_axes_0 = const()[name = string("op_884_axes_0"), val = tensor([1])]; - tensor var_884_cast_fp16 = expand_dims(axes = var_884_axes_0, x = var_812_cast_fp16_4)[name = string("op_884_cast_fp16")]; - fp16 var_885_to_fp16 = const()[name = string("op_885_to_fp16"), val = fp16(0x1p+0)]; - tensor var_886_cast_fp16 = add(x = var_884_cast_fp16, y = var_885_to_fp16)[name = string("op_886_cast_fp16")]; - tensor var_887_cast_fp16 = mul(x = x_37_cast_fp16, y = var_886_cast_fp16)[name = string("op_887_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_812_cast_fp16_3)[name = string("op_888_cast_fp16")]; - tensor input_149_cast_fp16 = add(x = var_887_cast_fp16, y = var_888_cast_fp16)[name = string("input_149_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(27821952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28870592))))[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(28874752)))]; - tensor linear_38_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_149_cast_fp16)[name = string("linear_38_cast_fp16")]; - string input_153_mode_0 = const()[name = string("input_153_mode_0"), val = string("EXACT")]; - tensor input_153_cast_fp16 = gelu(mode = input_153_mode_0, x = linear_38_cast_fp16)[name = string("input_153_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(28878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29927552))))[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(29928640)))]; - tensor linear_39_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_153_cast_fp16)[name = string("linear_39_cast_fp16")]; - tensor var_901_cast_fp16 = mul(x = var_881_cast_fp16, y = linear_39_cast_fp16)[name = string("op_901_cast_fp16")]; - tensor input_161_cast_fp16 = add(x = input_147_cast_fp16, y = var_901_cast_fp16)[name = string("input_161_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(29929728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31502656))))[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(31508864)))]; - tensor linear_40_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_39_cast_fp16)[name = string("linear_40_cast_fp16")]; - tensor var_910_split_sizes_0 = const()[name = string("op_910_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_910_axis_0 = const()[name = string("op_910_axis_0"), val = int32(-1)]; - tensor var_910_cast_fp16_0, tensor var_910_cast_fp16_1, tensor var_910_cast_fp16_2, tensor var_910_cast_fp16_3, tensor var_910_cast_fp16_4, tensor var_910_cast_fp16_5 = split(axis = var_910_axis_0, split_sizes = var_910_split_sizes_0, x = linear_40_cast_fp16)[name = string("op_910_cast_fp16")]; - tensor var_916_axes_0 = const()[name = string("op_916_axes_0"), val = tensor([1])]; - tensor var_916_cast_fp16 = expand_dims(axes = var_916_axes_0, x = var_910_cast_fp16_2)[name = string("op_916_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_69_to_fp16, x = input_161_cast_fp16)[name = string("x_39_cast_fp16")]; - tensor var_919_axes_0 = const()[name = string("op_919_axes_0"), val = tensor([1])]; - tensor var_919_cast_fp16 = expand_dims(axes = var_919_axes_0, x = var_910_cast_fp16_1)[name = string("op_919_cast_fp16")]; - fp16 var_920_to_fp16 = const()[name = string("op_920_to_fp16"), val = fp16(0x1p+0)]; - tensor var_921_cast_fp16 = add(x = var_919_cast_fp16, y = var_920_to_fp16)[name = string("op_921_cast_fp16")]; - tensor var_922_cast_fp16 = mul(x = x_39_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_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_910_cast_fp16_0)[name = string("op_923_cast_fp16")]; - tensor x_41_cast_fp16 = add(x = var_922_cast_fp16, y = var_923_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(31515072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32301568))))[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(32304704)))]; - tensor linear_41_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_41_cast_fp16")]; - tensor var_935 = const()[name = string("op_935"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_13_cast_fp16 = reshape(shape = var_935, x = linear_41_cast_fp16)[name = string("qkv_13_cast_fp16")]; - tensor var_937_split_sizes_0 = const()[name = string("op_937_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_937_axis_0 = const()[name = string("op_937_axis_0"), val = int32(2)]; - tensor var_937_cast_fp16_0, tensor var_937_cast_fp16_1, tensor var_937_cast_fp16_2 = split(axis = var_937_axis_0, split_sizes = var_937_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_937_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_937_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_937_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_937_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; - tensor var_942_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_942_cast_fp16")]; - tensor var_944_axes_0 = const()[name = string("op_944_axes_0"), val = tensor([-1])]; - bool var_944_keep_dims_0 = const()[name = string("op_944_keep_dims_0"), val = bool(true)]; - tensor var_944_cast_fp16 = reduce_mean(axes = var_944_axes_0, keep_dims = var_944_keep_dims_0, x = var_942_cast_fp16)[name = string("op_944_cast_fp16")]; - fp16 var_945_to_fp16 = const()[name = string("op_945_to_fp16"), val = fp16(0x1p-23)]; - tensor var_946_cast_fp16 = add(x = var_944_cast_fp16, y = var_945_to_fp16)[name = string("op_946_cast_fp16")]; - fp32 var_947_epsilon_0 = const()[name = string("op_947_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_947_cast_fp16 = rsqrt(epsilon = var_947_epsilon_0, x = var_946_cast_fp16)[name = string("op_947_cast_fp16")]; - tensor var_948_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_947_cast_fp16)[name = string("op_948_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(32307840)))]; - tensor var_949_cast_fp16 = mul(x = var_948_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_949_cast_fp16")]; - tensor var_952_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_952_cast_fp16")]; - tensor var_954_axes_0 = const()[name = string("op_954_axes_0"), val = tensor([-1])]; - bool var_954_keep_dims_0 = const()[name = string("op_954_keep_dims_0"), val = bool(true)]; - tensor var_954_cast_fp16 = reduce_mean(axes = var_954_axes_0, keep_dims = var_954_keep_dims_0, x = var_952_cast_fp16)[name = string("op_954_cast_fp16")]; - fp16 var_955_to_fp16 = const()[name = string("op_955_to_fp16"), val = fp16(0x1p-23)]; - tensor var_956_cast_fp16 = add(x = var_954_cast_fp16, y = var_955_to_fp16)[name = string("op_956_cast_fp16")]; - fp32 var_957_epsilon_0 = const()[name = string("op_957_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_957_cast_fp16 = rsqrt(epsilon = var_957_epsilon_0, x = var_956_cast_fp16)[name = string("op_957_cast_fp16")]; - tensor var_958_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_957_cast_fp16)[name = string("op_958_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(32308032)))]; - tensor var_959_cast_fp16 = mul(x = var_958_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_959_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_959_cast_fp16)[name = string("transpose_131")]; - tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_949_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_970_perm_0 = const()[name = string("op_970_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_971 = const()[name = string("op_971"), val = tensor([1, 352, 512])]; - tensor var_970_cast_fp16 = transpose(perm = var_970_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; - tensor input_163_cast_fp16 = reshape(shape = var_971, x = var_970_cast_fp16)[name = string("input_163_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(32308224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32570432))))[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(32571520)))]; - tensor linear_42_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_163_cast_fp16)[name = string("linear_42_cast_fp16")]; - tensor var_977_cast_fp16 = mul(x = var_916_cast_fp16, y = linear_42_cast_fp16)[name = string("op_977_cast_fp16")]; - tensor input_167_cast_fp16 = add(x = input_161_cast_fp16, y = var_977_cast_fp16)[name = string("input_167_cast_fp16")]; - tensor var_979_axes_0 = const()[name = string("op_979_axes_0"), val = tensor([1])]; - tensor var_979_cast_fp16 = expand_dims(axes = var_979_axes_0, x = var_910_cast_fp16_5)[name = string("op_979_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_69_to_fp16, x = input_167_cast_fp16)[name = string("x_43_cast_fp16")]; - tensor var_982_axes_0 = const()[name = string("op_982_axes_0"), val = tensor([1])]; - tensor var_982_cast_fp16 = expand_dims(axes = var_982_axes_0, x = var_910_cast_fp16_4)[name = string("op_982_cast_fp16")]; - fp16 var_983_to_fp16 = const()[name = string("op_983_to_fp16"), val = fp16(0x1p+0)]; - tensor var_984_cast_fp16 = add(x = var_982_cast_fp16, y = var_983_to_fp16)[name = string("op_984_cast_fp16")]; - tensor var_985_cast_fp16 = mul(x = x_43_cast_fp16, y = var_984_cast_fp16)[name = string("op_985_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_910_cast_fp16_3)[name = string("op_986_cast_fp16")]; - tensor input_169_cast_fp16 = add(x = var_985_cast_fp16, y = var_986_cast_fp16)[name = string("input_169_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(32572608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33621248))))[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(33625408)))]; - tensor linear_43_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_169_cast_fp16)[name = string("linear_43_cast_fp16")]; - string input_173_mode_0 = const()[name = string("input_173_mode_0"), val = string("EXACT")]; - tensor input_173_cast_fp16 = gelu(mode = input_173_mode_0, x = linear_43_cast_fp16)[name = string("input_173_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(33629568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34678208))))[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(34679296)))]; - tensor linear_44_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_173_cast_fp16)[name = string("linear_44_cast_fp16")]; - tensor var_999_cast_fp16 = mul(x = var_979_cast_fp16, y = linear_44_cast_fp16)[name = string("op_999_cast_fp16")]; - tensor input_181_cast_fp16 = add(x = input_167_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_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(34680384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36253312))))[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(36259520)))]; - tensor linear_45_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_39_cast_fp16)[name = string("linear_45_cast_fp16")]; - tensor var_1008_split_sizes_0 = const()[name = string("op_1008_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1008_axis_0 = const()[name = string("op_1008_axis_0"), val = int32(-1)]; - tensor var_1008_cast_fp16_0, tensor var_1008_cast_fp16_1, tensor var_1008_cast_fp16_2, tensor var_1008_cast_fp16_3, tensor var_1008_cast_fp16_4, tensor var_1008_cast_fp16_5 = split(axis = var_1008_axis_0, split_sizes = var_1008_split_sizes_0, x = linear_45_cast_fp16)[name = string("op_1008_cast_fp16")]; - tensor var_1014_axes_0 = const()[name = string("op_1014_axes_0"), val = tensor([1])]; - tensor var_1014_cast_fp16 = expand_dims(axes = var_1014_axes_0, x = var_1008_cast_fp16_2)[name = string("op_1014_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_69_to_fp16, x = input_181_cast_fp16)[name = string("x_45_cast_fp16")]; - tensor var_1017_axes_0 = const()[name = string("op_1017_axes_0"), val = tensor([1])]; - tensor var_1017_cast_fp16 = expand_dims(axes = var_1017_axes_0, x = var_1008_cast_fp16_1)[name = string("op_1017_cast_fp16")]; - fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1019_cast_fp16 = add(x = var_1017_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; - tensor var_1020_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_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_1008_cast_fp16_0)[name = string("op_1021_cast_fp16")]; - tensor x_47_cast_fp16 = add(x = var_1020_cast_fp16, y = var_1021_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(36265728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37052224))))[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(37055360)))]; - tensor linear_46_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_46_cast_fp16")]; - tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_15_cast_fp16 = reshape(shape = var_1033, x = linear_46_cast_fp16)[name = string("qkv_15_cast_fp16")]; - tensor var_1035_split_sizes_0 = const()[name = string("op_1035_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1035_axis_0 = const()[name = string("op_1035_axis_0"), val = int32(2)]; - tensor var_1035_cast_fp16_0, tensor var_1035_cast_fp16_1, tensor var_1035_cast_fp16_2 = split(axis = var_1035_axis_0, split_sizes = var_1035_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1035_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_1035_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_1035_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_1035_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; - tensor var_1040_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1040_cast_fp16")]; - tensor var_1042_axes_0 = const()[name = string("op_1042_axes_0"), val = tensor([-1])]; - bool var_1042_keep_dims_0 = const()[name = string("op_1042_keep_dims_0"), val = bool(true)]; - tensor var_1042_cast_fp16 = reduce_mean(axes = var_1042_axes_0, keep_dims = var_1042_keep_dims_0, x = var_1040_cast_fp16)[name = string("op_1042_cast_fp16")]; - fp16 var_1043_to_fp16 = const()[name = string("op_1043_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1044_cast_fp16 = add(x = var_1042_cast_fp16, y = var_1043_to_fp16)[name = string("op_1044_cast_fp16")]; - fp32 var_1045_epsilon_0 = const()[name = string("op_1045_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1045_cast_fp16 = rsqrt(epsilon = var_1045_epsilon_0, x = var_1044_cast_fp16)[name = string("op_1045_cast_fp16")]; - tensor var_1046_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1045_cast_fp16)[name = string("op_1046_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(37058496)))]; - tensor var_1047_cast_fp16 = mul(x = var_1046_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1047_cast_fp16")]; - tensor var_1050_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1050_cast_fp16")]; - tensor var_1052_axes_0 = const()[name = string("op_1052_axes_0"), val = tensor([-1])]; - bool var_1052_keep_dims_0 = const()[name = string("op_1052_keep_dims_0"), val = bool(true)]; - tensor var_1052_cast_fp16 = reduce_mean(axes = var_1052_axes_0, keep_dims = var_1052_keep_dims_0, x = var_1050_cast_fp16)[name = string("op_1052_cast_fp16")]; - fp16 var_1053_to_fp16 = const()[name = string("op_1053_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1054_cast_fp16 = add(x = var_1052_cast_fp16, y = var_1053_to_fp16)[name = string("op_1054_cast_fp16")]; - fp32 var_1055_epsilon_0 = const()[name = string("op_1055_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1055_cast_fp16 = rsqrt(epsilon = var_1055_epsilon_0, x = var_1054_cast_fp16)[name = string("op_1055_cast_fp16")]; - tensor var_1056_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1055_cast_fp16)[name = string("op_1056_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(37058688)))]; - tensor var_1057_cast_fp16 = mul(x = var_1056_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1057_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_1057_cast_fp16)[name = string("transpose_127")]; - tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1047_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_1068_perm_0 = const()[name = string("op_1068_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1069 = const()[name = string("op_1069"), val = tensor([1, 352, 512])]; - tensor var_1068_cast_fp16 = transpose(perm = var_1068_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; - tensor input_183_cast_fp16 = reshape(shape = var_1069, x = var_1068_cast_fp16)[name = string("input_183_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(37058880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37321088))))[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(37322176)))]; - tensor linear_47_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_183_cast_fp16)[name = string("linear_47_cast_fp16")]; - tensor var_1075_cast_fp16 = mul(x = var_1014_cast_fp16, y = linear_47_cast_fp16)[name = string("op_1075_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_181_cast_fp16, y = var_1075_cast_fp16)[name = string("input_187_cast_fp16")]; - tensor var_1077_axes_0 = const()[name = string("op_1077_axes_0"), val = tensor([1])]; - tensor var_1077_cast_fp16 = expand_dims(axes = var_1077_axes_0, x = var_1008_cast_fp16_5)[name = string("op_1077_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_69_to_fp16, x = input_187_cast_fp16)[name = string("x_49_cast_fp16")]; - tensor var_1080_axes_0 = const()[name = string("op_1080_axes_0"), val = tensor([1])]; - tensor var_1080_cast_fp16 = expand_dims(axes = var_1080_axes_0, x = var_1008_cast_fp16_4)[name = string("op_1080_cast_fp16")]; - fp16 var_1081_to_fp16 = const()[name = string("op_1081_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1082_cast_fp16 = add(x = var_1080_cast_fp16, y = var_1081_to_fp16)[name = string("op_1082_cast_fp16")]; - tensor var_1083_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1082_cast_fp16)[name = string("op_1083_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_1008_cast_fp16_3)[name = string("op_1084_cast_fp16")]; - tensor input_189_cast_fp16 = add(x = var_1083_cast_fp16, y = var_1084_cast_fp16)[name = string("input_189_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(37323264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38371904))))[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(38376064)))]; - tensor linear_48_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_189_cast_fp16)[name = string("linear_48_cast_fp16")]; - string input_193_mode_0 = const()[name = string("input_193_mode_0"), val = string("EXACT")]; - tensor input_193_cast_fp16 = gelu(mode = input_193_mode_0, x = linear_48_cast_fp16)[name = string("input_193_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(38380224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39428864))))[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(39429952)))]; - tensor linear_49_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_193_cast_fp16)[name = string("linear_49_cast_fp16")]; - tensor var_1097_cast_fp16 = mul(x = var_1077_cast_fp16, y = linear_49_cast_fp16)[name = string("op_1097_cast_fp16")]; - tensor input_201_cast_fp16 = add(x = input_187_cast_fp16, y = var_1097_cast_fp16)[name = string("input_201_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(39431040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41003968))))[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(41010176)))]; - tensor linear_50_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_39_cast_fp16)[name = string("linear_50_cast_fp16")]; - tensor var_1106_split_sizes_0 = const()[name = string("op_1106_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1106_axis_0 = const()[name = string("op_1106_axis_0"), val = int32(-1)]; - tensor var_1106_cast_fp16_0, tensor var_1106_cast_fp16_1, tensor var_1106_cast_fp16_2, tensor var_1106_cast_fp16_3, tensor var_1106_cast_fp16_4, tensor var_1106_cast_fp16_5 = split(axis = var_1106_axis_0, split_sizes = var_1106_split_sizes_0, x = linear_50_cast_fp16)[name = string("op_1106_cast_fp16")]; - tensor var_1112_axes_0 = const()[name = string("op_1112_axes_0"), val = tensor([1])]; - tensor var_1112_cast_fp16 = expand_dims(axes = var_1112_axes_0, x = var_1106_cast_fp16_2)[name = string("op_1112_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_69_to_fp16, x = input_201_cast_fp16)[name = string("x_51_cast_fp16")]; - tensor var_1115_axes_0 = const()[name = string("op_1115_axes_0"), val = tensor([1])]; - tensor var_1115_cast_fp16 = expand_dims(axes = var_1115_axes_0, x = var_1106_cast_fp16_1)[name = string("op_1115_cast_fp16")]; - fp16 var_1116_to_fp16 = const()[name = string("op_1116_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1117_cast_fp16 = add(x = var_1115_cast_fp16, y = var_1116_to_fp16)[name = string("op_1117_cast_fp16")]; - tensor var_1118_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1117_cast_fp16)[name = string("op_1118_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_1106_cast_fp16_0)[name = string("op_1119_cast_fp16")]; - tensor x_53_cast_fp16 = add(x = var_1118_cast_fp16, y = var_1119_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(41016384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41802880))))[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(41806016)))]; - tensor linear_51_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_51_cast_fp16")]; - tensor var_1131 = const()[name = string("op_1131"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_17_cast_fp16 = reshape(shape = var_1131, x = linear_51_cast_fp16)[name = string("qkv_17_cast_fp16")]; - tensor var_1133_split_sizes_0 = const()[name = string("op_1133_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1133_axis_0 = const()[name = string("op_1133_axis_0"), val = int32(2)]; - tensor var_1133_cast_fp16_0, tensor var_1133_cast_fp16_1, tensor var_1133_cast_fp16_2 = split(axis = var_1133_axis_0, split_sizes = var_1133_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1133_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_1133_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_1133_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_1133_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; - tensor var_1138_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1138_cast_fp16")]; - tensor var_1140_axes_0 = const()[name = string("op_1140_axes_0"), val = tensor([-1])]; - bool var_1140_keep_dims_0 = const()[name = string("op_1140_keep_dims_0"), val = bool(true)]; - tensor var_1140_cast_fp16 = reduce_mean(axes = var_1140_axes_0, keep_dims = var_1140_keep_dims_0, x = var_1138_cast_fp16)[name = string("op_1140_cast_fp16")]; - fp16 var_1141_to_fp16 = const()[name = string("op_1141_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1142_cast_fp16 = add(x = var_1140_cast_fp16, y = var_1141_to_fp16)[name = string("op_1142_cast_fp16")]; - fp32 var_1143_epsilon_0 = const()[name = string("op_1143_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1143_cast_fp16 = rsqrt(epsilon = var_1143_epsilon_0, x = var_1142_cast_fp16)[name = string("op_1143_cast_fp16")]; - tensor var_1144_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1143_cast_fp16)[name = string("op_1144_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(41809152)))]; - tensor var_1145_cast_fp16 = mul(x = var_1144_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1145_cast_fp16")]; - tensor var_1148_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1148_cast_fp16")]; - tensor var_1150_axes_0 = const()[name = string("op_1150_axes_0"), val = tensor([-1])]; - bool var_1150_keep_dims_0 = const()[name = string("op_1150_keep_dims_0"), val = bool(true)]; - tensor var_1150_cast_fp16 = reduce_mean(axes = var_1150_axes_0, keep_dims = var_1150_keep_dims_0, x = var_1148_cast_fp16)[name = string("op_1150_cast_fp16")]; - fp16 var_1151_to_fp16 = const()[name = string("op_1151_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1152_cast_fp16 = add(x = var_1150_cast_fp16, y = var_1151_to_fp16)[name = string("op_1152_cast_fp16")]; - fp32 var_1153_epsilon_0 = const()[name = string("op_1153_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1153_cast_fp16 = rsqrt(epsilon = var_1153_epsilon_0, x = var_1152_cast_fp16)[name = string("op_1153_cast_fp16")]; - tensor var_1154_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1153_cast_fp16)[name = string("op_1154_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(41809344)))]; - tensor var_1155_cast_fp16 = mul(x = var_1154_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1155_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_1155_cast_fp16)[name = string("transpose_123")]; - tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1145_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_1166_perm_0 = const()[name = string("op_1166_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1167 = const()[name = string("op_1167"), val = tensor([1, 352, 512])]; - tensor var_1166_cast_fp16 = transpose(perm = var_1166_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; - tensor input_203_cast_fp16 = reshape(shape = var_1167, x = var_1166_cast_fp16)[name = string("input_203_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(41809536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42071744))))[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(42072832)))]; - tensor linear_52_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_203_cast_fp16)[name = string("linear_52_cast_fp16")]; - tensor var_1173_cast_fp16 = mul(x = var_1112_cast_fp16, y = linear_52_cast_fp16)[name = string("op_1173_cast_fp16")]; - tensor input_207_cast_fp16 = add(x = input_201_cast_fp16, y = var_1173_cast_fp16)[name = string("input_207_cast_fp16")]; - tensor var_1175_axes_0 = const()[name = string("op_1175_axes_0"), val = tensor([1])]; - tensor var_1175_cast_fp16 = expand_dims(axes = var_1175_axes_0, x = var_1106_cast_fp16_5)[name = string("op_1175_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_69_to_fp16, x = input_207_cast_fp16)[name = string("x_55_cast_fp16")]; - tensor var_1178_axes_0 = const()[name = string("op_1178_axes_0"), val = tensor([1])]; - tensor var_1178_cast_fp16 = expand_dims(axes = var_1178_axes_0, x = var_1106_cast_fp16_4)[name = string("op_1178_cast_fp16")]; - fp16 var_1179_to_fp16 = const()[name = string("op_1179_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1180_cast_fp16 = add(x = var_1178_cast_fp16, y = var_1179_to_fp16)[name = string("op_1180_cast_fp16")]; - tensor var_1181_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1180_cast_fp16)[name = string("op_1181_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_1106_cast_fp16_3)[name = string("op_1182_cast_fp16")]; - tensor input_209_cast_fp16 = add(x = var_1181_cast_fp16, y = var_1182_cast_fp16)[name = string("input_209_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(42073920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43122560))))[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(43126720)))]; - tensor linear_53_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_209_cast_fp16)[name = string("linear_53_cast_fp16")]; - string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("EXACT")]; - tensor input_213_cast_fp16 = gelu(mode = input_213_mode_0, x = linear_53_cast_fp16)[name = string("input_213_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(43130880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44179520))))[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(44180608)))]; - tensor linear_54_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_213_cast_fp16)[name = string("linear_54_cast_fp16")]; - tensor var_1195_cast_fp16 = mul(x = var_1175_cast_fp16, y = linear_54_cast_fp16)[name = string("op_1195_cast_fp16")]; - tensor input_221_cast_fp16 = add(x = input_207_cast_fp16, y = var_1195_cast_fp16)[name = string("input_221_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(44181696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45754624))))[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(45760832)))]; - tensor linear_55_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_39_cast_fp16)[name = string("linear_55_cast_fp16")]; - tensor var_1204_split_sizes_0 = const()[name = string("op_1204_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1204_axis_0 = const()[name = string("op_1204_axis_0"), val = int32(-1)]; - tensor var_1204_cast_fp16_0, tensor var_1204_cast_fp16_1, tensor var_1204_cast_fp16_2, tensor var_1204_cast_fp16_3, tensor var_1204_cast_fp16_4, tensor var_1204_cast_fp16_5 = split(axis = var_1204_axis_0, split_sizes = var_1204_split_sizes_0, x = linear_55_cast_fp16)[name = string("op_1204_cast_fp16")]; - tensor var_1210_axes_0 = const()[name = string("op_1210_axes_0"), val = tensor([1])]; - tensor var_1210_cast_fp16 = expand_dims(axes = var_1210_axes_0, x = var_1204_cast_fp16_2)[name = string("op_1210_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_69_to_fp16, x = input_221_cast_fp16)[name = string("x_57_cast_fp16")]; - tensor var_1213_axes_0 = const()[name = string("op_1213_axes_0"), val = tensor([1])]; - tensor var_1213_cast_fp16 = expand_dims(axes = var_1213_axes_0, x = var_1204_cast_fp16_1)[name = string("op_1213_cast_fp16")]; - fp16 var_1214_to_fp16 = const()[name = string("op_1214_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1215_cast_fp16 = add(x = var_1213_cast_fp16, y = var_1214_to_fp16)[name = string("op_1215_cast_fp16")]; - tensor var_1216_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1215_cast_fp16)[name = string("op_1216_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_1204_cast_fp16_0)[name = string("op_1217_cast_fp16")]; - tensor x_59_cast_fp16 = add(x = var_1216_cast_fp16, y = var_1217_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(45767040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46553536))))[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(46556672)))]; - tensor linear_56_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_56_cast_fp16")]; - tensor var_1229 = const()[name = string("op_1229"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_19_cast_fp16 = reshape(shape = var_1229, x = linear_56_cast_fp16)[name = string("qkv_19_cast_fp16")]; - tensor var_1231_split_sizes_0 = const()[name = string("op_1231_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1231_axis_0 = const()[name = string("op_1231_axis_0"), val = int32(2)]; - tensor var_1231_cast_fp16_0, tensor var_1231_cast_fp16_1, tensor var_1231_cast_fp16_2 = split(axis = var_1231_axis_0, split_sizes = var_1231_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1231_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_1231_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_1231_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_1231_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; - tensor var_1236_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1236_cast_fp16")]; - tensor var_1238_axes_0 = const()[name = string("op_1238_axes_0"), val = tensor([-1])]; - bool var_1238_keep_dims_0 = const()[name = string("op_1238_keep_dims_0"), val = bool(true)]; - tensor var_1238_cast_fp16 = reduce_mean(axes = var_1238_axes_0, keep_dims = var_1238_keep_dims_0, x = var_1236_cast_fp16)[name = string("op_1238_cast_fp16")]; - fp16 var_1239_to_fp16 = const()[name = string("op_1239_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1240_cast_fp16 = add(x = var_1238_cast_fp16, y = var_1239_to_fp16)[name = string("op_1240_cast_fp16")]; - fp32 var_1241_epsilon_0 = const()[name = string("op_1241_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1241_cast_fp16 = rsqrt(epsilon = var_1241_epsilon_0, x = var_1240_cast_fp16)[name = string("op_1241_cast_fp16")]; - tensor var_1242_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1241_cast_fp16)[name = string("op_1242_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(46559808)))]; - tensor var_1243_cast_fp16 = mul(x = var_1242_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1243_cast_fp16")]; - tensor var_1246_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1246_cast_fp16")]; - tensor var_1248_axes_0 = const()[name = string("op_1248_axes_0"), val = tensor([-1])]; - bool var_1248_keep_dims_0 = const()[name = string("op_1248_keep_dims_0"), val = bool(true)]; - tensor var_1248_cast_fp16 = reduce_mean(axes = var_1248_axes_0, keep_dims = var_1248_keep_dims_0, x = var_1246_cast_fp16)[name = string("op_1248_cast_fp16")]; - fp16 var_1249_to_fp16 = const()[name = string("op_1249_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1250_cast_fp16 = add(x = var_1248_cast_fp16, y = var_1249_to_fp16)[name = string("op_1250_cast_fp16")]; - fp32 var_1251_epsilon_0 = const()[name = string("op_1251_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1251_cast_fp16 = rsqrt(epsilon = var_1251_epsilon_0, x = var_1250_cast_fp16)[name = string("op_1251_cast_fp16")]; - tensor var_1252_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1251_cast_fp16)[name = string("op_1252_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(46560000)))]; - tensor var_1253_cast_fp16 = mul(x = var_1252_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1253_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_1253_cast_fp16)[name = string("transpose_119")]; - tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1243_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_1264_perm_0 = const()[name = string("op_1264_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1265 = const()[name = string("op_1265"), val = tensor([1, 352, 512])]; - tensor var_1264_cast_fp16 = transpose(perm = var_1264_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; - tensor input_223_cast_fp16 = reshape(shape = var_1265, x = var_1264_cast_fp16)[name = string("input_223_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(46560192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46822400))))[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(46823488)))]; - tensor linear_57_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_223_cast_fp16)[name = string("linear_57_cast_fp16")]; - tensor var_1271_cast_fp16 = mul(x = var_1210_cast_fp16, y = linear_57_cast_fp16)[name = string("op_1271_cast_fp16")]; - tensor input_227_cast_fp16 = add(x = input_221_cast_fp16, y = var_1271_cast_fp16)[name = string("input_227_cast_fp16")]; - tensor var_1273_axes_0 = const()[name = string("op_1273_axes_0"), val = tensor([1])]; - tensor var_1273_cast_fp16 = expand_dims(axes = var_1273_axes_0, x = var_1204_cast_fp16_5)[name = string("op_1273_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_69_to_fp16, x = input_227_cast_fp16)[name = string("x_61_cast_fp16")]; - tensor var_1276_axes_0 = const()[name = string("op_1276_axes_0"), val = tensor([1])]; - tensor var_1276_cast_fp16 = expand_dims(axes = var_1276_axes_0, x = var_1204_cast_fp16_4)[name = string("op_1276_cast_fp16")]; - fp16 var_1277_to_fp16 = const()[name = string("op_1277_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1278_cast_fp16 = add(x = var_1276_cast_fp16, y = var_1277_to_fp16)[name = string("op_1278_cast_fp16")]; - tensor var_1279_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1278_cast_fp16)[name = string("op_1279_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_1204_cast_fp16_3)[name = string("op_1280_cast_fp16")]; - tensor input_229_cast_fp16 = add(x = var_1279_cast_fp16, y = var_1280_cast_fp16)[name = string("input_229_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(46824576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47873216))))[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(47877376)))]; - tensor linear_58_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_229_cast_fp16)[name = string("linear_58_cast_fp16")]; - string input_233_mode_0 = const()[name = string("input_233_mode_0"), val = string("EXACT")]; - tensor input_233_cast_fp16 = gelu(mode = input_233_mode_0, x = linear_58_cast_fp16)[name = string("input_233_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(47881536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48930176))))[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(48931264)))]; - tensor linear_59_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_233_cast_fp16)[name = string("linear_59_cast_fp16")]; - tensor var_1293_cast_fp16 = mul(x = var_1273_cast_fp16, y = linear_59_cast_fp16)[name = string("op_1293_cast_fp16")]; - tensor input_241_cast_fp16 = add(x = input_227_cast_fp16, y = var_1293_cast_fp16)[name = string("input_241_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(48932352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50505280))))[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(50511488)))]; - tensor linear_60_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_39_cast_fp16)[name = string("linear_60_cast_fp16")]; - tensor var_1302_split_sizes_0 = const()[name = string("op_1302_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1302_axis_0 = const()[name = string("op_1302_axis_0"), val = int32(-1)]; - tensor var_1302_cast_fp16_0, tensor var_1302_cast_fp16_1, tensor var_1302_cast_fp16_2, tensor var_1302_cast_fp16_3, tensor var_1302_cast_fp16_4, tensor var_1302_cast_fp16_5 = split(axis = var_1302_axis_0, split_sizes = var_1302_split_sizes_0, x = linear_60_cast_fp16)[name = string("op_1302_cast_fp16")]; - tensor var_1308_axes_0 = const()[name = string("op_1308_axes_0"), val = tensor([1])]; - tensor var_1308_cast_fp16 = expand_dims(axes = var_1308_axes_0, x = var_1302_cast_fp16_2)[name = string("op_1308_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_69_to_fp16, x = input_241_cast_fp16)[name = string("x_63_cast_fp16")]; - tensor var_1311_axes_0 = const()[name = string("op_1311_axes_0"), val = tensor([1])]; - tensor var_1311_cast_fp16 = expand_dims(axes = var_1311_axes_0, x = var_1302_cast_fp16_1)[name = string("op_1311_cast_fp16")]; - fp16 var_1312_to_fp16 = const()[name = string("op_1312_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1313_cast_fp16 = add(x = var_1311_cast_fp16, y = var_1312_to_fp16)[name = string("op_1313_cast_fp16")]; - tensor var_1314_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1313_cast_fp16)[name = string("op_1314_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_1302_cast_fp16_0)[name = string("op_1315_cast_fp16")]; - tensor x_65_cast_fp16 = add(x = var_1314_cast_fp16, y = var_1315_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(50517696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51304192))))[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(51307328)))]; - tensor linear_61_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_61_cast_fp16")]; - tensor var_1327 = const()[name = string("op_1327"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_21_cast_fp16 = reshape(shape = var_1327, x = linear_61_cast_fp16)[name = string("qkv_21_cast_fp16")]; - tensor var_1329_split_sizes_0 = const()[name = string("op_1329_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1329_axis_0 = const()[name = string("op_1329_axis_0"), val = int32(2)]; - tensor var_1329_cast_fp16_0, tensor var_1329_cast_fp16_1, tensor var_1329_cast_fp16_2 = split(axis = var_1329_axis_0, split_sizes = var_1329_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1329_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_1329_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_1329_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_1329_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; - tensor var_1334_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1334_cast_fp16")]; - tensor var_1336_axes_0 = const()[name = string("op_1336_axes_0"), val = tensor([-1])]; - bool var_1336_keep_dims_0 = const()[name = string("op_1336_keep_dims_0"), val = bool(true)]; - tensor var_1336_cast_fp16 = reduce_mean(axes = var_1336_axes_0, keep_dims = var_1336_keep_dims_0, x = var_1334_cast_fp16)[name = string("op_1336_cast_fp16")]; - fp16 var_1337_to_fp16 = const()[name = string("op_1337_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1338_cast_fp16 = add(x = var_1336_cast_fp16, y = var_1337_to_fp16)[name = string("op_1338_cast_fp16")]; - fp32 var_1339_epsilon_0 = const()[name = string("op_1339_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1339_cast_fp16 = rsqrt(epsilon = var_1339_epsilon_0, x = var_1338_cast_fp16)[name = string("op_1339_cast_fp16")]; - tensor var_1340_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1339_cast_fp16)[name = string("op_1340_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(51310464)))]; - tensor var_1341_cast_fp16 = mul(x = var_1340_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1341_cast_fp16")]; - tensor var_1344_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1344_cast_fp16")]; - tensor var_1346_axes_0 = const()[name = string("op_1346_axes_0"), val = tensor([-1])]; - bool var_1346_keep_dims_0 = const()[name = string("op_1346_keep_dims_0"), val = bool(true)]; - tensor var_1346_cast_fp16 = reduce_mean(axes = var_1346_axes_0, keep_dims = var_1346_keep_dims_0, x = var_1344_cast_fp16)[name = string("op_1346_cast_fp16")]; - fp16 var_1347_to_fp16 = const()[name = string("op_1347_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1348_cast_fp16 = add(x = var_1346_cast_fp16, y = var_1347_to_fp16)[name = string("op_1348_cast_fp16")]; - fp32 var_1349_epsilon_0 = const()[name = string("op_1349_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1349_cast_fp16 = rsqrt(epsilon = var_1349_epsilon_0, x = var_1348_cast_fp16)[name = string("op_1349_cast_fp16")]; - tensor var_1350_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1349_cast_fp16)[name = string("op_1350_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(51310656)))]; - tensor var_1351_cast_fp16 = mul(x = var_1350_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1351_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_1351_cast_fp16)[name = string("transpose_115")]; - tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1341_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_1362_perm_0 = const()[name = string("op_1362_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1363 = const()[name = string("op_1363"), val = tensor([1, 352, 512])]; - tensor var_1362_cast_fp16 = transpose(perm = var_1362_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; - tensor input_243_cast_fp16 = reshape(shape = var_1363, x = var_1362_cast_fp16)[name = string("input_243_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(51310848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51573056))))[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(51574144)))]; - tensor linear_62_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_243_cast_fp16)[name = string("linear_62_cast_fp16")]; - tensor var_1369_cast_fp16 = mul(x = var_1308_cast_fp16, y = linear_62_cast_fp16)[name = string("op_1369_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_241_cast_fp16, y = var_1369_cast_fp16)[name = string("input_247_cast_fp16")]; - tensor var_1371_axes_0 = const()[name = string("op_1371_axes_0"), val = tensor([1])]; - tensor var_1371_cast_fp16 = expand_dims(axes = var_1371_axes_0, x = var_1302_cast_fp16_5)[name = string("op_1371_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_69_to_fp16, x = input_247_cast_fp16)[name = string("x_67_cast_fp16")]; - tensor var_1374_axes_0 = const()[name = string("op_1374_axes_0"), val = tensor([1])]; - tensor var_1374_cast_fp16 = expand_dims(axes = var_1374_axes_0, x = var_1302_cast_fp16_4)[name = string("op_1374_cast_fp16")]; - fp16 var_1375_to_fp16 = const()[name = string("op_1375_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1376_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1375_to_fp16)[name = string("op_1376_cast_fp16")]; - tensor var_1377_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1376_cast_fp16)[name = string("op_1377_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_1302_cast_fp16_3)[name = string("op_1378_cast_fp16")]; - tensor input_249_cast_fp16 = add(x = var_1377_cast_fp16, y = var_1378_cast_fp16)[name = string("input_249_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(51575232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52623872))))[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(52628032)))]; - tensor linear_63_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_249_cast_fp16)[name = string("linear_63_cast_fp16")]; - string input_253_mode_0 = const()[name = string("input_253_mode_0"), val = string("EXACT")]; - tensor input_253_cast_fp16 = gelu(mode = input_253_mode_0, x = linear_63_cast_fp16)[name = string("input_253_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(52632192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53680832))))[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(53681920)))]; - tensor linear_64_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_253_cast_fp16)[name = string("linear_64_cast_fp16")]; - tensor var_1391_cast_fp16 = mul(x = var_1371_cast_fp16, y = linear_64_cast_fp16)[name = string("op_1391_cast_fp16")]; - tensor input_261_cast_fp16 = add(x = input_247_cast_fp16, y = var_1391_cast_fp16)[name = string("input_261_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(53683008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55255936))))[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(55262144)))]; - tensor linear_65_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_39_cast_fp16)[name = string("linear_65_cast_fp16")]; - tensor var_1400_split_sizes_0 = const()[name = string("op_1400_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1400_axis_0 = const()[name = string("op_1400_axis_0"), val = int32(-1)]; - tensor var_1400_cast_fp16_0, tensor var_1400_cast_fp16_1, tensor var_1400_cast_fp16_2, tensor var_1400_cast_fp16_3, tensor var_1400_cast_fp16_4, tensor var_1400_cast_fp16_5 = split(axis = var_1400_axis_0, split_sizes = var_1400_split_sizes_0, x = linear_65_cast_fp16)[name = string("op_1400_cast_fp16")]; - tensor var_1406_axes_0 = const()[name = string("op_1406_axes_0"), val = tensor([1])]; - tensor var_1406_cast_fp16 = expand_dims(axes = var_1406_axes_0, x = var_1400_cast_fp16_2)[name = string("op_1406_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_69_to_fp16, x = input_261_cast_fp16)[name = string("x_69_cast_fp16")]; - tensor var_1409_axes_0 = const()[name = string("op_1409_axes_0"), val = tensor([1])]; - tensor var_1409_cast_fp16 = expand_dims(axes = var_1409_axes_0, x = var_1400_cast_fp16_1)[name = string("op_1409_cast_fp16")]; - fp16 var_1410_to_fp16 = const()[name = string("op_1410_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1411_cast_fp16 = add(x = var_1409_cast_fp16, y = var_1410_to_fp16)[name = string("op_1411_cast_fp16")]; - tensor var_1412_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1411_cast_fp16)[name = string("op_1412_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_1400_cast_fp16_0)[name = string("op_1413_cast_fp16")]; - tensor x_71_cast_fp16 = add(x = var_1412_cast_fp16, y = var_1413_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(55268352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56054848))))[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(56057984)))]; - tensor linear_66_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_66_cast_fp16")]; - tensor var_1425 = const()[name = string("op_1425"), val = tensor([1, 352, 3, 8, 64])]; - tensor qkv_23_cast_fp16 = reshape(shape = var_1425, x = linear_66_cast_fp16)[name = string("qkv_23_cast_fp16")]; - tensor var_1427_split_sizes_0 = const()[name = string("op_1427_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1427_axis_0 = const()[name = string("op_1427_axis_0"), val = int32(2)]; - tensor var_1427_cast_fp16_0, tensor var_1427_cast_fp16_1, tensor var_1427_cast_fp16_2 = split(axis = var_1427_axis_0, split_sizes = var_1427_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1427_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_1427_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_1427_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_1427_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; - tensor var_1432_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1432_cast_fp16")]; - tensor var_1434_axes_0 = const()[name = string("op_1434_axes_0"), val = tensor([-1])]; - bool var_1434_keep_dims_0 = const()[name = string("op_1434_keep_dims_0"), val = bool(true)]; - tensor var_1434_cast_fp16 = reduce_mean(axes = var_1434_axes_0, keep_dims = var_1434_keep_dims_0, x = var_1432_cast_fp16)[name = string("op_1434_cast_fp16")]; - fp16 var_1435_to_fp16 = const()[name = string("op_1435_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1436_cast_fp16 = add(x = var_1434_cast_fp16, y = var_1435_to_fp16)[name = string("op_1436_cast_fp16")]; - fp32 var_1437_epsilon_0 = const()[name = string("op_1437_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1437_cast_fp16 = rsqrt(epsilon = var_1437_epsilon_0, x = var_1436_cast_fp16)[name = string("op_1437_cast_fp16")]; - tensor var_1438_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1437_cast_fp16)[name = string("op_1438_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(56061120)))]; - tensor var_1439_cast_fp16 = mul(x = var_1438_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1439_cast_fp16")]; - tensor var_1442_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1442_cast_fp16")]; - tensor var_1444_axes_0 = const()[name = string("op_1444_axes_0"), val = tensor([-1])]; - bool var_1444_keep_dims_0 = const()[name = string("op_1444_keep_dims_0"), val = bool(true)]; - tensor var_1444_cast_fp16 = reduce_mean(axes = var_1444_axes_0, keep_dims = var_1444_keep_dims_0, x = var_1442_cast_fp16)[name = string("op_1444_cast_fp16")]; - fp16 var_1445_to_fp16 = const()[name = string("op_1445_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1446_cast_fp16 = add(x = var_1444_cast_fp16, y = var_1445_to_fp16)[name = string("op_1446_cast_fp16")]; - fp32 var_1447_epsilon_0 = const()[name = string("op_1447_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1447_cast_fp16 = rsqrt(epsilon = var_1447_epsilon_0, x = var_1446_cast_fp16)[name = string("op_1447_cast_fp16")]; - tensor var_1448_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1447_cast_fp16)[name = string("op_1448_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(56061312)))]; - tensor var_1449_cast_fp16 = mul(x = var_1448_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1449_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_1449_cast_fp16)[name = string("transpose_111")]; - tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1439_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_1460_perm_0 = const()[name = string("op_1460_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1461 = const()[name = string("op_1461"), val = tensor([1, 352, 512])]; - tensor var_1460_cast_fp16 = transpose(perm = var_1460_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; - tensor input_263_cast_fp16 = reshape(shape = var_1461, x = var_1460_cast_fp16)[name = string("input_263_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(56061504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56323712))))[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(56324800)))]; - tensor linear_67_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_263_cast_fp16)[name = string("linear_67_cast_fp16")]; - tensor var_1467_cast_fp16 = mul(x = var_1406_cast_fp16, y = linear_67_cast_fp16)[name = string("op_1467_cast_fp16")]; - tensor input_267_cast_fp16 = add(x = input_261_cast_fp16, y = var_1467_cast_fp16)[name = string("input_267_cast_fp16")]; - tensor var_1469_axes_0 = const()[name = string("op_1469_axes_0"), val = tensor([1])]; - tensor var_1469_cast_fp16 = expand_dims(axes = var_1469_axes_0, x = var_1400_cast_fp16_5)[name = string("op_1469_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_69_to_fp16, x = input_267_cast_fp16)[name = string("x_73_cast_fp16")]; - tensor var_1472_axes_0 = const()[name = string("op_1472_axes_0"), val = tensor([1])]; - tensor var_1472_cast_fp16 = expand_dims(axes = var_1472_axes_0, x = var_1400_cast_fp16_4)[name = string("op_1472_cast_fp16")]; - fp16 var_1473_to_fp16 = const()[name = string("op_1473_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1474_cast_fp16 = add(x = var_1472_cast_fp16, y = var_1473_to_fp16)[name = string("op_1474_cast_fp16")]; - tensor var_1475_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1474_cast_fp16)[name = string("op_1475_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_1400_cast_fp16_3)[name = string("op_1476_cast_fp16")]; - tensor input_269_cast_fp16 = add(x = var_1475_cast_fp16, y = var_1476_cast_fp16)[name = string("input_269_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(56325888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57374528))))[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(57378688)))]; - tensor linear_68_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_269_cast_fp16)[name = string("linear_68_cast_fp16")]; - string input_273_mode_0 = const()[name = string("input_273_mode_0"), val = string("EXACT")]; - tensor input_273_cast_fp16 = gelu(mode = input_273_mode_0, x = linear_68_cast_fp16)[name = string("input_273_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(57382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58431488))))[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(58432576)))]; - tensor linear_69_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_273_cast_fp16)[name = string("linear_69_cast_fp16")]; - tensor var_1489_cast_fp16 = mul(x = var_1469_cast_fp16, y = linear_69_cast_fp16)[name = string("op_1489_cast_fp16")]; - tensor input_281_cast_fp16 = add(x = input_267_cast_fp16, y = var_1489_cast_fp16)[name = string("input_281_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(58433664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58958016))))[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(58960128)))]; - tensor linear_70_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_39_cast_fp16)[name = string("linear_70_cast_fp16")]; - tensor var_1495_split_sizes_0 = const()[name = string("op_1495_split_sizes_0"), val = tensor([512, 512])]; - int32 var_1495_axis_0 = const()[name = string("op_1495_axis_0"), val = int32(-1)]; - tensor var_1495_cast_fp16_0, tensor var_1495_cast_fp16_1 = split(axis = var_1495_axis_0, split_sizes = var_1495_split_sizes_0, x = linear_70_cast_fp16)[name = string("op_1495_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_69_to_fp16, x = input_281_cast_fp16)[name = string("x_cast_fp16")]; - tensor var_1499_axes_0 = const()[name = string("op_1499_axes_0"), val = tensor([1])]; - tensor var_1499_cast_fp16 = expand_dims(axes = var_1499_axes_0, x = var_1495_cast_fp16_1)[name = string("op_1499_cast_fp16")]; - fp16 var_1500_to_fp16 = const()[name = string("op_1500_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1501_cast_fp16 = add(x = var_1499_cast_fp16, y = var_1500_to_fp16)[name = string("op_1501_cast_fp16")]; - tensor var_1502_cast_fp16 = mul(x = x_cast_fp16, y = var_1501_cast_fp16)[name = string("op_1502_cast_fp16")]; - tensor var_1503_axes_0 = const()[name = string("op_1503_axes_0"), val = tensor([1])]; - tensor var_1503_cast_fp16 = expand_dims(axes = var_1503_axes_0, x = var_1495_cast_fp16_0)[name = string("op_1503_cast_fp16")]; - tensor encoded_cast_fp16 = add(x = var_1502_cast_fp16, y = var_1503_cast_fp16)[name = string("encoded_cast_fp16")]; - tensor input_283_begin_0 = const()[name = string("input_283_begin_0"), val = tensor([0, 96, 0])]; - tensor input_283_end_0 = const()[name = string("input_283_end_0"), val = tensor([1, 352, 512])]; - tensor input_283_end_mask_0 = const()[name = string("input_283_end_mask_0"), val = tensor([true, true, true])]; - tensor input_283_cast_fp16 = slice_by_index(begin = input_283_begin_0, end = input_283_end_0, end_mask = input_283_end_mask_0, x = encoded_cast_fp16)[name = string("input_283_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(58962240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59224448))))[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(59225536)))]; - tensor linear_71_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_71_cast_fp16")]; - string input_287_mode_0 = const()[name = string("input_287_mode_0"), val = string("EXACT")]; - tensor input_287_cast_fp16 = gelu(mode = input_287_mode_0, x = linear_71_cast_fp16)[name = string("input_287_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(59226624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59238976))))[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(59239104)))]; - tensor linear_72_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_72_cast_fp16")]; - tensor var_1517_perm_0 = const()[name = string("op_1517_perm_0"), val = tensor([0, 2, 1])]; - string var_1517_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1517_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; - tensor var_1517_cast_fp16 = transpose(perm = var_1517_perm_0, x = linear_72_cast_fp16)[name = string("transpose_108")]; - tensor velocity = cast(dtype = var_1517_cast_fp16_to_fp32_dtype_0, x = var_1517_cast_fp16)[name = string("cast_1")]; - } -> (velocity); - func vector_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 prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { - int32 var_33 = const()[name = string("op_33"), val = int32(0)]; - tensor present = not_equal(x = delivery_present, y = var_33)[name = string("present")]; - int32 var_35 = const()[name = string("op_35"), val = int32(0)]; - tensor latent_mask_1 = not_equal(x = latent_mask, y = var_35)[name = string("latent_mask")]; - int32 var_37 = const()[name = string("op_37"), val = int32(0)]; - tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_37)[name = string("prefix_mask_1")]; - int32 var_58 = const()[name = string("op_58"), val = int32(-1)]; - int32 var_73 = const()[name = string("op_73"), val = int32(1)]; - int32 var_87 = const()[name = string("op_87"), val = int32(7)]; - tensor var_88 = mul(x = voice_id, y = var_87)[name = string("op_88")]; - tensor index = add(x = var_88, 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(35968))))[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_20")]; - tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_19")]; - 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(70)]; - 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_18")]; - tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_17")]; - 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(70)]; - 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(36224)))]; - 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_16")]; - 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(40768)))]; - 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(45312)))]; - 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(49856)))]; - 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(50112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62464))))[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(63552)))]; - tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_15")]; - 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_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_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109248))))[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_14")]; - tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; - tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; - int32 var_150_axis_0 = const()[name = string("op_150_axis_0"), val = int32(0)]; - int32 var_150_batch_dims_0 = const()[name = string("op_150_batch_dims_0"), val = int32(0)]; - bool var_150_validate_indices_0 = const()[name = string("op_150_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(109504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154624))))[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_13")]; - tensor var_150_cast_fp16_cast_uint16 = gather(axis = var_150_axis_0, batch_dims = var_150_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_150_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_150_cast_fp16_cast_uint16")]; - int32 var_155_axis_0 = const()[name = string("op_155_axis_0"), val = int32(0)]; - int32 var_155_batch_dims_0 = const()[name = string("op_155_batch_dims_0"), val = int32(0)]; - bool var_155_validate_indices_0 = const()[name = string("op_155_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(154880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187712))))[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_12")]; - tensor var_155_cast_fp16_cast_uint16 = gather(axis = var_155_axis_0, batch_dims = var_155_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_155_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_155_cast_fp16_cast_uint16")]; - tensor hidden_7_cast_fp16 = add(x = var_150_cast_fp16_cast_uint16, y = var_155_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_10")]; - 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_11")]; - 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(187904)))]; - 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(190016)))]; - 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_166_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_166_cast_fp16")]; - tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_166_cast_fp16)[name = string("hidden_11_cast_fp16")]; - int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(0)]; - int32 var_170_batch_dims_0 = const()[name = string("op_170_batch_dims_0"), val = int32(0)]; - bool var_170_validate_indices_0 = const()[name = string("op_170_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(191104)))]; - 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_9")]; - tensor var_170_cast_fp16_cast_uint16 = gather(axis = var_170_axis_0, batch_dims = var_170_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_170_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_170_cast_fp16_cast_uint16")]; - tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_170_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; - tensor op_174_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59371136))), scale = tensor([[[0x1.21p-5]]]))[name = string("op_174_to_fp16_quantized")]; - tensor hidden_17_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_174_to_fp16_quantized)[name = string("hidden_17_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(261888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267072))))[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(267200)))]; - 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_186_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_186_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_58, interleave = input_15_interleave_0, values = (var_186_cast_fp16, present_to_fp16))[name = string("input_15_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(274432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279616))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; - 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(280704)))]; - tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; - tensor var_204_axes_0 = const()[name = string("op_204_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_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = time_to_fp16)[name = string("op_204_cast_fp16")]; - tensor var_205_to_fp16 = const()[name = string("op_205_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281792)))]; - tensor phases_1_cast_fp16 = mul(x = var_204_cast_fp16, y = var_205_to_fp16)[name = string("phases_1_cast_fp16")]; - fp16 var_207_to_fp16 = const()[name = string("op_207_to_fp16"), val = fp16(0x1.92p+2)]; - tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_207_to_fp16)[name = string("phases_cast_fp16")]; - tensor var_209_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_209_cast_fp16")]; - tensor var_210_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_210_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_58, interleave = input_21_interleave_0, values = (var_209_cast_fp16, var_210_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(282368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(544576))))[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(545664)))]; - 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(546752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808960))))[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(810048)))]; - 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_220_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_220_cast_fp16")]; - tensor var_221_cast_fp16 = add(x = var_220_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_221_cast_fp16")]; - tensor var_222_cast_fp16 = add(x = var_221_cast_fp16, y = linear_2_cast_fp16)[name = string("op_222_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(811136)))]; - tensor cond_1_cast_fp16 = add(x = var_222_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; - fp16 var_61_to_fp16 = const()[name = string("op_61_to_fp16"), val = fp16(0x0p+0)]; - fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x1p+0)]; - tensor clip_0_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; - tensor clip_1_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; - tensor var_236_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_236_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(812224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(828672))))[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(829760)))]; - 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_236_cast_fp16)[name = string("linear_5_cast_fp16")]; - tensor var_242_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_242_cast_fp16")]; - tensor var_243_axes_0 = const()[name = string("op_243_axes_0"), val = tensor([-1])]; - tensor var_243_cast_fp16 = expand_dims(axes = var_243_axes_0, x = clip_1_cast_fp16)[name = string("op_243_cast_fp16")]; - tensor var_244_cast_fp16 = mul(x = var_242_cast_fp16, y = var_243_cast_fp16)[name = string("op_244_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(830848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(847296))))[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_244_cast_fp16)[name = string("linear_6_cast_fp16")]; - tensor var_250_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_250_cast_fp16")]; - tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_250_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_260_axes_0 = const()[name = string("op_260_axes_0"), val = tensor([-1])]; - tensor var_260_cast_fp16 = expand_dims(axes = var_260_axes_0, x = mask_7_cast_fp16)[name = string("op_260_cast_fp16")]; - tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_260_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(848384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110592))))[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(1111680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1373888))))[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_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_37_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_37_cast_fp16")]; - tensor input_33_perm_0 = const()[name = string("input_33_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_33_cast_fp16 = transpose(perm = input_33_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; - tensor linear_9_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_9_cast_fp16")]; - tensor op_277_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1374976))), scale = tensor([[[0x1.278p-5]]]))[name = string("op_277_to_fp16_quantized")]; - tensor context_3_cast_fp16 = add(x = linear_9_cast_fp16, y = op_277_to_fp16_quantized)[name = string("context_3_cast_fp16")]; - tensor var_280_to_fp16 = const()[name = string("op_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1424192)))]; - tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_280_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_73, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_17_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_73, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; - fp16 var_288_to_fp16 = const()[name = string("op_288_to_fp16"), val = fp16(0x1p-3)]; - tensor input_41_cast_fp16 = mul(x = x_1_cast_fp16, y = var_288_to_fp16)[name = string("input_41_cast_fp16")]; - tensor input_39_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("input_39_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(1425280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2998208))))[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(3004416)))]; - tensor linear_10_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_39_cast_fp16)[name = string("linear_10_cast_fp16")]; - tensor var_322_split_sizes_0 = const()[name = string("op_322_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_322_axis_0 = const()[name = string("op_322_axis_0"), val = int32(-1)]; - tensor var_322_cast_fp16_0, tensor var_322_cast_fp16_1, tensor var_322_cast_fp16_2, tensor var_322_cast_fp16_3, tensor var_322_cast_fp16_4, tensor var_322_cast_fp16_5 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = linear_10_cast_fp16)[name = string("op_322_cast_fp16")]; - tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; - tensor var_328_cast_fp16 = expand_dims(axes = var_328_axes_0, x = var_322_cast_fp16_2)[name = string("op_328_cast_fp16")]; - tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; - fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x1.8p-23)]; - tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_69_to_fp16, x = input_41_cast_fp16)[name = string("x_3_cast_fp16")]; - tensor var_331_axes_0 = const()[name = string("op_331_axes_0"), val = tensor([1])]; - tensor var_331_cast_fp16 = expand_dims(axes = var_331_axes_0, x = var_322_cast_fp16_1)[name = string("op_331_cast_fp16")]; - fp16 var_332_to_fp16 = const()[name = string("op_332_to_fp16"), val = fp16(0x1p+0)]; - tensor var_333_cast_fp16 = add(x = var_331_cast_fp16, y = var_332_to_fp16)[name = string("op_333_cast_fp16")]; - tensor var_334_cast_fp16 = mul(x = x_3_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_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_322_cast_fp16_0)[name = string("op_335_cast_fp16")]; - tensor x_5_cast_fp16 = add(x = var_334_cast_fp16, y = var_335_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(3010624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3797120))))[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(3800256)))]; - tensor linear_11_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_11_cast_fp16")]; - tensor var_347 = const()[name = string("op_347"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_1_cast_fp16 = reshape(shape = var_347, x = linear_11_cast_fp16)[name = string("qkv_1_cast_fp16")]; - tensor var_349_split_sizes_0 = const()[name = string("op_349_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_349_axis_0 = const()[name = string("op_349_axis_0"), val = int32(2)]; - tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1, tensor var_349_cast_fp16_2 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_349_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_349_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_349_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_349_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; - tensor var_354_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_354_cast_fp16")]; - tensor var_356_axes_0 = const()[name = string("op_356_axes_0"), val = tensor([-1])]; - bool var_356_keep_dims_0 = const()[name = string("op_356_keep_dims_0"), val = bool(true)]; - tensor var_356_cast_fp16 = reduce_mean(axes = var_356_axes_0, keep_dims = var_356_keep_dims_0, x = var_354_cast_fp16)[name = string("op_356_cast_fp16")]; - fp16 var_357_to_fp16 = const()[name = string("op_357_to_fp16"), val = fp16(0x1p-23)]; - tensor var_358_cast_fp16 = add(x = var_356_cast_fp16, y = var_357_to_fp16)[name = string("op_358_cast_fp16")]; - fp32 var_359_epsilon_0 = const()[name = string("op_359_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_359_cast_fp16 = rsqrt(epsilon = var_359_epsilon_0, x = var_358_cast_fp16)[name = string("op_359_cast_fp16")]; - tensor var_360_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_359_cast_fp16)[name = string("op_360_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(3803392)))]; - tensor var_361_cast_fp16 = mul(x = var_360_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_361_cast_fp16")]; - tensor var_364_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_364_cast_fp16")]; - tensor var_366_axes_0 = const()[name = string("op_366_axes_0"), val = tensor([-1])]; - bool var_366_keep_dims_0 = const()[name = string("op_366_keep_dims_0"), val = bool(true)]; - tensor var_366_cast_fp16 = reduce_mean(axes = var_366_axes_0, keep_dims = var_366_keep_dims_0, x = var_364_cast_fp16)[name = string("op_366_cast_fp16")]; - fp16 var_367_to_fp16 = const()[name = string("op_367_to_fp16"), val = fp16(0x1p-23)]; - tensor var_368_cast_fp16 = add(x = var_366_cast_fp16, y = var_367_to_fp16)[name = string("op_368_cast_fp16")]; - fp32 var_369_epsilon_0 = const()[name = string("op_369_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_369_cast_fp16 = rsqrt(epsilon = var_369_epsilon_0, x = var_368_cast_fp16)[name = string("op_369_cast_fp16")]; - tensor var_370_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_369_cast_fp16)[name = string("op_370_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(3803584)))]; - tensor var_371_cast_fp16 = mul(x = var_370_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_371_cast_fp16")]; - tensor var_376 = const()[name = string("op_376"), val = tensor([1, 1, 1, 480])]; - tensor var_377 = reshape(shape = var_376, x = valid_mask)[name = string("op_377")]; - tensor var_379 = logical_not(x = var_377)[name = string("op_379")]; - fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(-inf)]; - tensor var_375_to_fp16 = const()[name = string("op_375_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59567808)))]; - tensor attn_mask_1_cast_fp16 = select(a = var_68_to_fp16, b = var_375_to_fp16, cond = var_379)[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_371_cast_fp16)[name = string("transpose_155")]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_361_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_382_perm_0 = const()[name = string("op_382_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_383 = const()[name = string("op_383"), val = tensor([1, 480, 512])]; - tensor var_382_cast_fp16 = transpose(perm = var_382_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; - tensor input_43_cast_fp16 = reshape(shape = var_383, x = var_382_cast_fp16)[name = string("input_43_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(3804288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4066496))))[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(4067584)))]; - tensor linear_12_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_43_cast_fp16)[name = string("linear_12_cast_fp16")]; - tensor var_389_cast_fp16 = mul(x = var_328_cast_fp16, y = linear_12_cast_fp16)[name = string("op_389_cast_fp16")]; - tensor input_47_cast_fp16 = add(x = input_41_cast_fp16, y = var_389_cast_fp16)[name = string("input_47_cast_fp16")]; - tensor var_391_axes_0 = const()[name = string("op_391_axes_0"), val = tensor([1])]; - tensor var_391_cast_fp16 = expand_dims(axes = var_391_axes_0, x = var_322_cast_fp16_5)[name = string("op_391_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_69_to_fp16, x = input_47_cast_fp16)[name = string("x_7_cast_fp16")]; - tensor var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor([1])]; - tensor var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_322_cast_fp16_4)[name = string("op_394_cast_fp16")]; - fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p+0)]; - tensor var_396_cast_fp16 = add(x = var_394_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; - tensor var_397_cast_fp16 = mul(x = x_7_cast_fp16, y = var_396_cast_fp16)[name = string("op_397_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_322_cast_fp16_3)[name = string("op_398_cast_fp16")]; - tensor input_49_cast_fp16 = add(x = var_397_cast_fp16, y = var_398_cast_fp16)[name = string("input_49_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(4068672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5117312))))[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(5121472)))]; - tensor linear_13_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_49_cast_fp16)[name = string("linear_13_cast_fp16")]; - string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; - tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_13_cast_fp16)[name = string("input_53_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(5125632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6174272))))[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(6175360)))]; - tensor linear_14_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_53_cast_fp16)[name = string("linear_14_cast_fp16")]; - tensor var_411_cast_fp16 = mul(x = var_391_cast_fp16, y = linear_14_cast_fp16)[name = string("op_411_cast_fp16")]; - tensor input_61_cast_fp16 = add(x = input_47_cast_fp16, y = var_411_cast_fp16)[name = string("input_61_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(6176448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7749376))))[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(7755584)))]; - tensor linear_15_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_39_cast_fp16)[name = string("linear_15_cast_fp16")]; - tensor var_420_split_sizes_0 = const()[name = string("op_420_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_420_axis_0 = const()[name = string("op_420_axis_0"), val = int32(-1)]; - tensor var_420_cast_fp16_0, tensor var_420_cast_fp16_1, tensor var_420_cast_fp16_2, tensor var_420_cast_fp16_3, tensor var_420_cast_fp16_4, tensor var_420_cast_fp16_5 = split(axis = var_420_axis_0, split_sizes = var_420_split_sizes_0, x = linear_15_cast_fp16)[name = string("op_420_cast_fp16")]; - tensor var_426_axes_0 = const()[name = string("op_426_axes_0"), val = tensor([1])]; - tensor var_426_cast_fp16 = expand_dims(axes = var_426_axes_0, x = var_420_cast_fp16_2)[name = string("op_426_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_69_to_fp16, x = input_61_cast_fp16)[name = string("x_9_cast_fp16")]; - tensor var_429_axes_0 = const()[name = string("op_429_axes_0"), val = tensor([1])]; - tensor var_429_cast_fp16 = expand_dims(axes = var_429_axes_0, x = var_420_cast_fp16_1)[name = string("op_429_cast_fp16")]; - fp16 var_430_to_fp16 = const()[name = string("op_430_to_fp16"), val = fp16(0x1p+0)]; - tensor var_431_cast_fp16 = add(x = var_429_cast_fp16, y = var_430_to_fp16)[name = string("op_431_cast_fp16")]; - tensor var_432_cast_fp16 = mul(x = x_9_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_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_420_cast_fp16_0)[name = string("op_433_cast_fp16")]; - tensor x_11_cast_fp16 = add(x = var_432_cast_fp16, y = var_433_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(7761792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548288))))[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(8551424)))]; - tensor linear_16_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_16_cast_fp16")]; - tensor var_445 = const()[name = string("op_445"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_3_cast_fp16 = reshape(shape = var_445, x = linear_16_cast_fp16)[name = string("qkv_3_cast_fp16")]; - tensor var_447_split_sizes_0 = const()[name = string("op_447_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_447_axis_0 = const()[name = string("op_447_axis_0"), val = int32(2)]; - tensor var_447_cast_fp16_0, tensor var_447_cast_fp16_1, tensor var_447_cast_fp16_2 = split(axis = var_447_axis_0, split_sizes = var_447_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_447_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_447_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_447_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_447_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; - tensor var_452_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_452_cast_fp16")]; - tensor var_454_axes_0 = const()[name = string("op_454_axes_0"), val = tensor([-1])]; - bool var_454_keep_dims_0 = const()[name = string("op_454_keep_dims_0"), val = bool(true)]; - tensor var_454_cast_fp16 = reduce_mean(axes = var_454_axes_0, keep_dims = var_454_keep_dims_0, x = var_452_cast_fp16)[name = string("op_454_cast_fp16")]; - fp16 var_455_to_fp16 = const()[name = string("op_455_to_fp16"), val = fp16(0x1p-23)]; - tensor var_456_cast_fp16 = add(x = var_454_cast_fp16, y = var_455_to_fp16)[name = string("op_456_cast_fp16")]; - fp32 var_457_epsilon_0 = const()[name = string("op_457_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_457_cast_fp16 = rsqrt(epsilon = var_457_epsilon_0, x = var_456_cast_fp16)[name = string("op_457_cast_fp16")]; - tensor var_458_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_457_cast_fp16)[name = string("op_458_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(8554560)))]; - tensor var_459_cast_fp16 = mul(x = var_458_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_459_cast_fp16")]; - tensor var_462_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_462_cast_fp16")]; - tensor var_464_axes_0 = const()[name = string("op_464_axes_0"), val = tensor([-1])]; - bool var_464_keep_dims_0 = const()[name = string("op_464_keep_dims_0"), val = bool(true)]; - tensor var_464_cast_fp16 = reduce_mean(axes = var_464_axes_0, keep_dims = var_464_keep_dims_0, x = var_462_cast_fp16)[name = string("op_464_cast_fp16")]; - fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1p-23)]; - tensor var_466_cast_fp16 = add(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("op_466_cast_fp16")]; - fp32 var_467_epsilon_0 = const()[name = string("op_467_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_467_cast_fp16 = rsqrt(epsilon = var_467_epsilon_0, x = var_466_cast_fp16)[name = string("op_467_cast_fp16")]; - tensor var_468_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_467_cast_fp16)[name = string("op_468_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(8554752)))]; - tensor var_469_cast_fp16 = mul(x = var_468_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_469_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_469_cast_fp16)[name = string("transpose_151")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_459_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_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, 480, 512])]; - tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; - tensor input_63_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("input_63_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(8554944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8817152))))[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(8818240)))]; - tensor linear_17_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_63_cast_fp16)[name = string("linear_17_cast_fp16")]; - tensor var_487_cast_fp16 = mul(x = var_426_cast_fp16, y = linear_17_cast_fp16)[name = string("op_487_cast_fp16")]; - tensor input_67_cast_fp16 = add(x = input_61_cast_fp16, y = var_487_cast_fp16)[name = string("input_67_cast_fp16")]; - tensor var_489_axes_0 = const()[name = string("op_489_axes_0"), val = tensor([1])]; - tensor var_489_cast_fp16 = expand_dims(axes = var_489_axes_0, x = var_420_cast_fp16_5)[name = string("op_489_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_69_to_fp16, x = input_67_cast_fp16)[name = string("x_13_cast_fp16")]; - tensor var_492_axes_0 = const()[name = string("op_492_axes_0"), val = tensor([1])]; - tensor var_492_cast_fp16 = expand_dims(axes = var_492_axes_0, x = var_420_cast_fp16_4)[name = string("op_492_cast_fp16")]; - fp16 var_493_to_fp16 = const()[name = string("op_493_to_fp16"), val = fp16(0x1p+0)]; - tensor var_494_cast_fp16 = add(x = var_492_cast_fp16, y = var_493_to_fp16)[name = string("op_494_cast_fp16")]; - tensor var_495_cast_fp16 = mul(x = x_13_cast_fp16, y = var_494_cast_fp16)[name = string("op_495_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_420_cast_fp16_3)[name = string("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = var_495_cast_fp16, y = var_496_cast_fp16)[name = string("input_69_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(8819328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867968))))[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(9872128)))]; - tensor linear_18_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_69_cast_fp16)[name = string("linear_18_cast_fp16")]; - string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")]; - tensor input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = linear_18_cast_fp16)[name = string("input_73_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(9876288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10924928))))[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(10926016)))]; - tensor linear_19_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_73_cast_fp16)[name = string("linear_19_cast_fp16")]; - tensor var_509_cast_fp16 = mul(x = var_489_cast_fp16, y = linear_19_cast_fp16)[name = string("op_509_cast_fp16")]; - tensor input_81_cast_fp16 = add(x = input_67_cast_fp16, y = var_509_cast_fp16)[name = string("input_81_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(10927104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12500032))))[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(12506240)))]; - tensor linear_20_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_39_cast_fp16)[name = string("linear_20_cast_fp16")]; - tensor var_518_split_sizes_0 = const()[name = string("op_518_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_518_axis_0 = const()[name = string("op_518_axis_0"), val = int32(-1)]; - tensor var_518_cast_fp16_0, tensor var_518_cast_fp16_1, tensor var_518_cast_fp16_2, tensor var_518_cast_fp16_3, tensor var_518_cast_fp16_4, tensor var_518_cast_fp16_5 = split(axis = var_518_axis_0, split_sizes = var_518_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_518_cast_fp16")]; - tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; - tensor var_524_cast_fp16 = expand_dims(axes = var_524_axes_0, x = var_518_cast_fp16_2)[name = string("op_524_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_69_to_fp16, x = input_81_cast_fp16)[name = string("x_15_cast_fp16")]; - tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([1])]; - tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = var_518_cast_fp16_1)[name = string("op_527_cast_fp16")]; - fp16 var_528_to_fp16 = const()[name = string("op_528_to_fp16"), val = fp16(0x1p+0)]; - tensor var_529_cast_fp16 = add(x = var_527_cast_fp16, y = var_528_to_fp16)[name = string("op_529_cast_fp16")]; - tensor var_530_cast_fp16 = mul(x = x_15_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_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_518_cast_fp16_0)[name = string("op_531_cast_fp16")]; - tensor x_17_cast_fp16 = add(x = var_530_cast_fp16, y = var_531_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(12512448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13298944))))[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(13302080)))]; - tensor linear_21_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_21_cast_fp16")]; - tensor var_543 = const()[name = string("op_543"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_5_cast_fp16 = reshape(shape = var_543, x = linear_21_cast_fp16)[name = string("qkv_5_cast_fp16")]; - tensor var_545_split_sizes_0 = const()[name = string("op_545_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_545_axis_0 = const()[name = string("op_545_axis_0"), val = int32(2)]; - tensor var_545_cast_fp16_0, tensor var_545_cast_fp16_1, tensor var_545_cast_fp16_2 = split(axis = var_545_axis_0, split_sizes = var_545_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_545_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_545_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_545_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_545_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; - tensor var_550_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_550_cast_fp16")]; - tensor var_552_axes_0 = const()[name = string("op_552_axes_0"), val = tensor([-1])]; - bool var_552_keep_dims_0 = const()[name = string("op_552_keep_dims_0"), val = bool(true)]; - tensor var_552_cast_fp16 = reduce_mean(axes = var_552_axes_0, keep_dims = var_552_keep_dims_0, x = var_550_cast_fp16)[name = string("op_552_cast_fp16")]; - fp16 var_553_to_fp16 = const()[name = string("op_553_to_fp16"), val = fp16(0x1p-23)]; - tensor var_554_cast_fp16 = add(x = var_552_cast_fp16, y = var_553_to_fp16)[name = string("op_554_cast_fp16")]; - fp32 var_555_epsilon_0 = const()[name = string("op_555_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_555_cast_fp16 = rsqrt(epsilon = var_555_epsilon_0, x = var_554_cast_fp16)[name = string("op_555_cast_fp16")]; - tensor var_556_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_555_cast_fp16)[name = string("op_556_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(13305216)))]; - tensor var_557_cast_fp16 = mul(x = var_556_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_557_cast_fp16")]; - tensor var_560_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_560_cast_fp16")]; - tensor var_562_axes_0 = const()[name = string("op_562_axes_0"), val = tensor([-1])]; - bool var_562_keep_dims_0 = const()[name = string("op_562_keep_dims_0"), val = bool(true)]; - tensor var_562_cast_fp16 = reduce_mean(axes = var_562_axes_0, keep_dims = var_562_keep_dims_0, x = var_560_cast_fp16)[name = string("op_562_cast_fp16")]; - fp16 var_563_to_fp16 = const()[name = string("op_563_to_fp16"), val = fp16(0x1p-23)]; - tensor var_564_cast_fp16 = add(x = var_562_cast_fp16, y = var_563_to_fp16)[name = string("op_564_cast_fp16")]; - fp32 var_565_epsilon_0 = const()[name = string("op_565_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_565_cast_fp16 = rsqrt(epsilon = var_565_epsilon_0, x = var_564_cast_fp16)[name = string("op_565_cast_fp16")]; - tensor var_566_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_565_cast_fp16)[name = string("op_566_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(13305408)))]; - tensor var_567_cast_fp16 = mul(x = var_566_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_567_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_567_cast_fp16)[name = string("transpose_147")]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_557_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_578_perm_0 = const()[name = string("op_578_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_579 = const()[name = string("op_579"), val = tensor([1, 480, 512])]; - tensor var_578_cast_fp16 = transpose(perm = var_578_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; - tensor input_83_cast_fp16 = reshape(shape = var_579, x = var_578_cast_fp16)[name = string("input_83_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(13305600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13567808))))[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(13568896)))]; - tensor linear_22_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_83_cast_fp16)[name = string("linear_22_cast_fp16")]; - tensor var_585_cast_fp16 = mul(x = var_524_cast_fp16, y = linear_22_cast_fp16)[name = string("op_585_cast_fp16")]; - tensor input_87_cast_fp16 = add(x = input_81_cast_fp16, y = var_585_cast_fp16)[name = string("input_87_cast_fp16")]; - tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([1])]; - tensor var_587_cast_fp16 = expand_dims(axes = var_587_axes_0, x = var_518_cast_fp16_5)[name = string("op_587_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_69_to_fp16, x = input_87_cast_fp16)[name = string("x_19_cast_fp16")]; - tensor var_590_axes_0 = const()[name = string("op_590_axes_0"), val = tensor([1])]; - tensor var_590_cast_fp16 = expand_dims(axes = var_590_axes_0, x = var_518_cast_fp16_4)[name = string("op_590_cast_fp16")]; - fp16 var_591_to_fp16 = const()[name = string("op_591_to_fp16"), val = fp16(0x1p+0)]; - tensor var_592_cast_fp16 = add(x = var_590_cast_fp16, y = var_591_to_fp16)[name = string("op_592_cast_fp16")]; - tensor var_593_cast_fp16 = mul(x = x_19_cast_fp16, y = var_592_cast_fp16)[name = string("op_593_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_518_cast_fp16_3)[name = string("op_594_cast_fp16")]; - tensor input_89_cast_fp16 = add(x = var_593_cast_fp16, y = var_594_cast_fp16)[name = string("input_89_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(13569984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14618624))))[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(14622784)))]; - tensor linear_23_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_89_cast_fp16)[name = string("linear_23_cast_fp16")]; - string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")]; - tensor input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = linear_23_cast_fp16)[name = string("input_93_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(14626944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15675584))))[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(15676672)))]; - tensor linear_24_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_93_cast_fp16)[name = string("linear_24_cast_fp16")]; - tensor var_607_cast_fp16 = mul(x = var_587_cast_fp16, y = linear_24_cast_fp16)[name = string("op_607_cast_fp16")]; - tensor input_101_cast_fp16 = add(x = input_87_cast_fp16, y = var_607_cast_fp16)[name = string("input_101_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(15677760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17250688))))[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(17256896)))]; - tensor linear_25_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_39_cast_fp16)[name = string("linear_25_cast_fp16")]; - tensor var_616_split_sizes_0 = const()[name = string("op_616_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_616_axis_0 = const()[name = string("op_616_axis_0"), val = int32(-1)]; - tensor var_616_cast_fp16_0, tensor var_616_cast_fp16_1, tensor var_616_cast_fp16_2, tensor var_616_cast_fp16_3, tensor var_616_cast_fp16_4, tensor var_616_cast_fp16_5 = split(axis = var_616_axis_0, split_sizes = var_616_split_sizes_0, x = linear_25_cast_fp16)[name = string("op_616_cast_fp16")]; - tensor var_622_axes_0 = const()[name = string("op_622_axes_0"), val = tensor([1])]; - tensor var_622_cast_fp16 = expand_dims(axes = var_622_axes_0, x = var_616_cast_fp16_2)[name = string("op_622_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_69_to_fp16, x = input_101_cast_fp16)[name = string("x_21_cast_fp16")]; - tensor var_625_axes_0 = const()[name = string("op_625_axes_0"), val = tensor([1])]; - tensor var_625_cast_fp16 = expand_dims(axes = var_625_axes_0, x = var_616_cast_fp16_1)[name = string("op_625_cast_fp16")]; - fp16 var_626_to_fp16 = const()[name = string("op_626_to_fp16"), val = fp16(0x1p+0)]; - tensor var_627_cast_fp16 = add(x = var_625_cast_fp16, y = var_626_to_fp16)[name = string("op_627_cast_fp16")]; - tensor var_628_cast_fp16 = mul(x = x_21_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_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_616_cast_fp16_0)[name = string("op_629_cast_fp16")]; - tensor x_23_cast_fp16 = add(x = var_628_cast_fp16, y = var_629_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(17263104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18049600))))[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(18052736)))]; - tensor linear_26_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_26_cast_fp16")]; - tensor var_641 = const()[name = string("op_641"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_7_cast_fp16 = reshape(shape = var_641, x = linear_26_cast_fp16)[name = string("qkv_7_cast_fp16")]; - tensor var_643_split_sizes_0 = const()[name = string("op_643_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_643_axis_0 = const()[name = string("op_643_axis_0"), val = int32(2)]; - tensor var_643_cast_fp16_0, tensor var_643_cast_fp16_1, tensor var_643_cast_fp16_2 = split(axis = var_643_axis_0, split_sizes = var_643_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_643_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_643_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_643_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_643_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; - tensor var_648_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_648_cast_fp16")]; - tensor var_650_axes_0 = const()[name = string("op_650_axes_0"), val = tensor([-1])]; - bool var_650_keep_dims_0 = const()[name = string("op_650_keep_dims_0"), val = bool(true)]; - tensor var_650_cast_fp16 = reduce_mean(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = string("op_650_cast_fp16")]; - fp16 var_651_to_fp16 = const()[name = string("op_651_to_fp16"), val = fp16(0x1p-23)]; - tensor var_652_cast_fp16 = add(x = var_650_cast_fp16, y = var_651_to_fp16)[name = string("op_652_cast_fp16")]; - fp32 var_653_epsilon_0 = const()[name = string("op_653_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_653_cast_fp16 = rsqrt(epsilon = var_653_epsilon_0, x = var_652_cast_fp16)[name = string("op_653_cast_fp16")]; - tensor var_654_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_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(18055872)))]; - tensor var_655_cast_fp16 = mul(x = var_654_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_655_cast_fp16")]; - tensor var_658_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_658_cast_fp16")]; - tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; - bool var_660_keep_dims_0 = const()[name = string("op_660_keep_dims_0"), val = bool(true)]; - tensor var_660_cast_fp16 = reduce_mean(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = string("op_660_cast_fp16")]; - fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-23)]; - tensor var_662_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("op_662_cast_fp16")]; - fp32 var_663_epsilon_0 = const()[name = string("op_663_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_663_cast_fp16 = rsqrt(epsilon = var_663_epsilon_0, x = var_662_cast_fp16)[name = string("op_663_cast_fp16")]; - tensor var_664_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_663_cast_fp16)[name = string("op_664_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(18056064)))]; - tensor var_665_cast_fp16 = mul(x = var_664_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_665_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_665_cast_fp16)[name = string("transpose_143")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_655_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_676_perm_0 = const()[name = string("op_676_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_677 = const()[name = string("op_677"), val = tensor([1, 480, 512])]; - tensor var_676_cast_fp16 = transpose(perm = var_676_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; - tensor input_103_cast_fp16 = reshape(shape = var_677, x = var_676_cast_fp16)[name = string("input_103_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(18056256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18318464))))[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(18319552)))]; - tensor linear_27_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_103_cast_fp16)[name = string("linear_27_cast_fp16")]; - tensor var_683_cast_fp16 = mul(x = var_622_cast_fp16, y = linear_27_cast_fp16)[name = string("op_683_cast_fp16")]; - tensor input_107_cast_fp16 = add(x = input_101_cast_fp16, y = var_683_cast_fp16)[name = string("input_107_cast_fp16")]; - tensor var_685_axes_0 = const()[name = string("op_685_axes_0"), val = tensor([1])]; - tensor var_685_cast_fp16 = expand_dims(axes = var_685_axes_0, x = var_616_cast_fp16_5)[name = string("op_685_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_69_to_fp16, x = input_107_cast_fp16)[name = string("x_25_cast_fp16")]; - tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([1])]; - tensor var_688_cast_fp16 = expand_dims(axes = var_688_axes_0, x = var_616_cast_fp16_4)[name = string("op_688_cast_fp16")]; - fp16 var_689_to_fp16 = const()[name = string("op_689_to_fp16"), val = fp16(0x1p+0)]; - tensor var_690_cast_fp16 = add(x = var_688_cast_fp16, y = var_689_to_fp16)[name = string("op_690_cast_fp16")]; - tensor var_691_cast_fp16 = mul(x = x_25_cast_fp16, y = var_690_cast_fp16)[name = string("op_691_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_616_cast_fp16_3)[name = string("op_692_cast_fp16")]; - tensor input_109_cast_fp16 = add(x = var_691_cast_fp16, y = var_692_cast_fp16)[name = string("input_109_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(18320640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19369280))))[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(19373440)))]; - tensor linear_28_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_109_cast_fp16)[name = string("linear_28_cast_fp16")]; - string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; - tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_28_cast_fp16)[name = string("input_113_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(19377600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20426240))))[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(20427328)))]; - tensor linear_29_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_113_cast_fp16)[name = string("linear_29_cast_fp16")]; - tensor var_705_cast_fp16 = mul(x = var_685_cast_fp16, y = linear_29_cast_fp16)[name = string("op_705_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_107_cast_fp16, y = var_705_cast_fp16)[name = string("input_121_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(20428416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001344))))[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(22007552)))]; - tensor linear_30_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_39_cast_fp16)[name = string("linear_30_cast_fp16")]; - tensor var_714_split_sizes_0 = const()[name = string("op_714_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_714_axis_0 = const()[name = string("op_714_axis_0"), val = int32(-1)]; - tensor var_714_cast_fp16_0, tensor var_714_cast_fp16_1, tensor var_714_cast_fp16_2, tensor var_714_cast_fp16_3, tensor var_714_cast_fp16_4, tensor var_714_cast_fp16_5 = split(axis = var_714_axis_0, split_sizes = var_714_split_sizes_0, x = linear_30_cast_fp16)[name = string("op_714_cast_fp16")]; - tensor var_720_axes_0 = const()[name = string("op_720_axes_0"), val = tensor([1])]; - tensor var_720_cast_fp16 = expand_dims(axes = var_720_axes_0, x = var_714_cast_fp16_2)[name = string("op_720_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_69_to_fp16, x = input_121_cast_fp16)[name = string("x_27_cast_fp16")]; - tensor var_723_axes_0 = const()[name = string("op_723_axes_0"), val = tensor([1])]; - tensor var_723_cast_fp16 = expand_dims(axes = var_723_axes_0, x = var_714_cast_fp16_1)[name = string("op_723_cast_fp16")]; - fp16 var_724_to_fp16 = const()[name = string("op_724_to_fp16"), val = fp16(0x1p+0)]; - tensor var_725_cast_fp16 = add(x = var_723_cast_fp16, y = var_724_to_fp16)[name = string("op_725_cast_fp16")]; - tensor var_726_cast_fp16 = mul(x = x_27_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_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_714_cast_fp16_0)[name = string("op_727_cast_fp16")]; - tensor x_29_cast_fp16 = add(x = var_726_cast_fp16, y = var_727_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(22013760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22800256))))[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(22803392)))]; - tensor linear_31_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_31_cast_fp16")]; - tensor var_739 = const()[name = string("op_739"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_9_cast_fp16 = reshape(shape = var_739, x = linear_31_cast_fp16)[name = string("qkv_9_cast_fp16")]; - tensor var_741_split_sizes_0 = const()[name = string("op_741_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_741_axis_0 = const()[name = string("op_741_axis_0"), val = int32(2)]; - tensor var_741_cast_fp16_0, tensor var_741_cast_fp16_1, tensor var_741_cast_fp16_2 = split(axis = var_741_axis_0, split_sizes = var_741_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_741_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_741_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_741_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_741_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; - tensor var_746_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_746_cast_fp16")]; - tensor var_748_axes_0 = const()[name = string("op_748_axes_0"), val = tensor([-1])]; - bool var_748_keep_dims_0 = const()[name = string("op_748_keep_dims_0"), val = bool(true)]; - tensor var_748_cast_fp16 = reduce_mean(axes = var_748_axes_0, keep_dims = var_748_keep_dims_0, x = var_746_cast_fp16)[name = string("op_748_cast_fp16")]; - fp16 var_749_to_fp16 = const()[name = string("op_749_to_fp16"), val = fp16(0x1p-23)]; - tensor var_750_cast_fp16 = add(x = var_748_cast_fp16, y = var_749_to_fp16)[name = string("op_750_cast_fp16")]; - fp32 var_751_epsilon_0 = const()[name = string("op_751_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_751_cast_fp16 = rsqrt(epsilon = var_751_epsilon_0, x = var_750_cast_fp16)[name = string("op_751_cast_fp16")]; - tensor var_752_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_751_cast_fp16)[name = string("op_752_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(22806528)))]; - tensor var_753_cast_fp16 = mul(x = var_752_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_753_cast_fp16")]; - tensor var_756_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_756_cast_fp16")]; - tensor var_758_axes_0 = const()[name = string("op_758_axes_0"), val = tensor([-1])]; - bool var_758_keep_dims_0 = const()[name = string("op_758_keep_dims_0"), val = bool(true)]; - tensor var_758_cast_fp16 = reduce_mean(axes = var_758_axes_0, keep_dims = var_758_keep_dims_0, x = var_756_cast_fp16)[name = string("op_758_cast_fp16")]; - fp16 var_759_to_fp16 = const()[name = string("op_759_to_fp16"), val = fp16(0x1p-23)]; - tensor var_760_cast_fp16 = add(x = var_758_cast_fp16, y = var_759_to_fp16)[name = string("op_760_cast_fp16")]; - fp32 var_761_epsilon_0 = const()[name = string("op_761_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_761_cast_fp16 = rsqrt(epsilon = var_761_epsilon_0, x = var_760_cast_fp16)[name = string("op_761_cast_fp16")]; - tensor var_762_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_761_cast_fp16)[name = string("op_762_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(22806720)))]; - tensor var_763_cast_fp16 = mul(x = var_762_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_763_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_763_cast_fp16)[name = string("transpose_139")]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_753_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_774_perm_0 = const()[name = string("op_774_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_775 = const()[name = string("op_775"), val = tensor([1, 480, 512])]; - tensor var_774_cast_fp16 = transpose(perm = var_774_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; - tensor input_123_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("input_123_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(22806912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23069120))))[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(23070208)))]; - tensor linear_32_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_123_cast_fp16)[name = string("linear_32_cast_fp16")]; - tensor var_781_cast_fp16 = mul(x = var_720_cast_fp16, y = linear_32_cast_fp16)[name = string("op_781_cast_fp16")]; - tensor input_127_cast_fp16 = add(x = input_121_cast_fp16, y = var_781_cast_fp16)[name = string("input_127_cast_fp16")]; - tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([1])]; - tensor var_783_cast_fp16 = expand_dims(axes = var_783_axes_0, x = var_714_cast_fp16_5)[name = string("op_783_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_69_to_fp16, x = input_127_cast_fp16)[name = string("x_31_cast_fp16")]; - tensor var_786_axes_0 = const()[name = string("op_786_axes_0"), val = tensor([1])]; - tensor var_786_cast_fp16 = expand_dims(axes = var_786_axes_0, x = var_714_cast_fp16_4)[name = string("op_786_cast_fp16")]; - fp16 var_787_to_fp16 = const()[name = string("op_787_to_fp16"), val = fp16(0x1p+0)]; - tensor var_788_cast_fp16 = add(x = var_786_cast_fp16, y = var_787_to_fp16)[name = string("op_788_cast_fp16")]; - tensor var_789_cast_fp16 = mul(x = x_31_cast_fp16, y = var_788_cast_fp16)[name = string("op_789_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_714_cast_fp16_3)[name = string("op_790_cast_fp16")]; - tensor input_129_cast_fp16 = add(x = var_789_cast_fp16, y = var_790_cast_fp16)[name = string("input_129_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(23071296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119936))))[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(24124096)))]; - tensor linear_33_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_129_cast_fp16)[name = string("linear_33_cast_fp16")]; - string input_133_mode_0 = const()[name = string("input_133_mode_0"), val = string("EXACT")]; - tensor input_133_cast_fp16 = gelu(mode = input_133_mode_0, x = linear_33_cast_fp16)[name = string("input_133_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(24128256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176896))))[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(25177984)))]; - tensor linear_34_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_133_cast_fp16)[name = string("linear_34_cast_fp16")]; - tensor var_803_cast_fp16 = mul(x = var_783_cast_fp16, y = linear_34_cast_fp16)[name = string("op_803_cast_fp16")]; - tensor input_141_cast_fp16 = add(x = input_127_cast_fp16, y = var_803_cast_fp16)[name = string("input_141_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(25179072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26752000))))[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(26758208)))]; - tensor linear_35_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_39_cast_fp16)[name = string("linear_35_cast_fp16")]; - tensor var_812_split_sizes_0 = const()[name = string("op_812_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_812_axis_0 = const()[name = string("op_812_axis_0"), val = int32(-1)]; - tensor var_812_cast_fp16_0, tensor var_812_cast_fp16_1, tensor var_812_cast_fp16_2, tensor var_812_cast_fp16_3, tensor var_812_cast_fp16_4, tensor var_812_cast_fp16_5 = split(axis = var_812_axis_0, split_sizes = var_812_split_sizes_0, x = linear_35_cast_fp16)[name = string("op_812_cast_fp16")]; - tensor var_818_axes_0 = const()[name = string("op_818_axes_0"), val = tensor([1])]; - tensor var_818_cast_fp16 = expand_dims(axes = var_818_axes_0, x = var_812_cast_fp16_2)[name = string("op_818_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_69_to_fp16, x = input_141_cast_fp16)[name = string("x_33_cast_fp16")]; - tensor var_821_axes_0 = const()[name = string("op_821_axes_0"), val = tensor([1])]; - tensor var_821_cast_fp16 = expand_dims(axes = var_821_axes_0, x = var_812_cast_fp16_1)[name = string("op_821_cast_fp16")]; - fp16 var_822_to_fp16 = const()[name = string("op_822_to_fp16"), val = fp16(0x1p+0)]; - tensor var_823_cast_fp16 = add(x = var_821_cast_fp16, y = var_822_to_fp16)[name = string("op_823_cast_fp16")]; - tensor var_824_cast_fp16 = mul(x = x_33_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_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_812_cast_fp16_0)[name = string("op_825_cast_fp16")]; - tensor x_35_cast_fp16 = add(x = var_824_cast_fp16, y = var_825_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(26764416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27550912))))[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(27554048)))]; - tensor linear_36_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_36_cast_fp16")]; - tensor var_837 = const()[name = string("op_837"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_11_cast_fp16 = reshape(shape = var_837, x = linear_36_cast_fp16)[name = string("qkv_11_cast_fp16")]; - tensor var_839_split_sizes_0 = const()[name = string("op_839_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_839_axis_0 = const()[name = string("op_839_axis_0"), val = int32(2)]; - tensor var_839_cast_fp16_0, tensor var_839_cast_fp16_1, tensor var_839_cast_fp16_2 = split(axis = var_839_axis_0, split_sizes = var_839_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_839_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_839_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_839_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_839_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; - tensor var_844_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_844_cast_fp16")]; - tensor var_846_axes_0 = const()[name = string("op_846_axes_0"), val = tensor([-1])]; - bool var_846_keep_dims_0 = const()[name = string("op_846_keep_dims_0"), val = bool(true)]; - tensor var_846_cast_fp16 = reduce_mean(axes = var_846_axes_0, keep_dims = var_846_keep_dims_0, x = var_844_cast_fp16)[name = string("op_846_cast_fp16")]; - fp16 var_847_to_fp16 = const()[name = string("op_847_to_fp16"), val = fp16(0x1p-23)]; - tensor var_848_cast_fp16 = add(x = var_846_cast_fp16, y = var_847_to_fp16)[name = string("op_848_cast_fp16")]; - fp32 var_849_epsilon_0 = const()[name = string("op_849_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_849_cast_fp16 = rsqrt(epsilon = var_849_epsilon_0, x = var_848_cast_fp16)[name = string("op_849_cast_fp16")]; - tensor var_850_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_849_cast_fp16)[name = string("op_850_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(27557184)))]; - tensor var_851_cast_fp16 = mul(x = var_850_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_851_cast_fp16")]; - tensor var_854_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_854_cast_fp16")]; - tensor var_856_axes_0 = const()[name = string("op_856_axes_0"), val = tensor([-1])]; - bool var_856_keep_dims_0 = const()[name = string("op_856_keep_dims_0"), val = bool(true)]; - tensor var_856_cast_fp16 = reduce_mean(axes = var_856_axes_0, keep_dims = var_856_keep_dims_0, x = var_854_cast_fp16)[name = string("op_856_cast_fp16")]; - fp16 var_857_to_fp16 = const()[name = string("op_857_to_fp16"), val = fp16(0x1p-23)]; - tensor var_858_cast_fp16 = add(x = var_856_cast_fp16, y = var_857_to_fp16)[name = string("op_858_cast_fp16")]; - fp32 var_859_epsilon_0 = const()[name = string("op_859_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_859_cast_fp16 = rsqrt(epsilon = var_859_epsilon_0, x = var_858_cast_fp16)[name = string("op_859_cast_fp16")]; - tensor var_860_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_859_cast_fp16)[name = string("op_860_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(27557376)))]; - tensor var_861_cast_fp16 = mul(x = var_860_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_861_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_861_cast_fp16)[name = string("transpose_135")]; - tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_851_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_872_perm_0 = const()[name = string("op_872_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_873 = const()[name = string("op_873"), val = tensor([1, 480, 512])]; - tensor var_872_cast_fp16 = transpose(perm = var_872_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; - tensor input_143_cast_fp16 = reshape(shape = var_873, x = var_872_cast_fp16)[name = string("input_143_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(27557568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27819776))))[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(27820864)))]; - tensor linear_37_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_143_cast_fp16)[name = string("linear_37_cast_fp16")]; - tensor var_879_cast_fp16 = mul(x = var_818_cast_fp16, y = linear_37_cast_fp16)[name = string("op_879_cast_fp16")]; - tensor input_147_cast_fp16 = add(x = input_141_cast_fp16, y = var_879_cast_fp16)[name = string("input_147_cast_fp16")]; - tensor var_881_axes_0 = const()[name = string("op_881_axes_0"), val = tensor([1])]; - tensor var_881_cast_fp16 = expand_dims(axes = var_881_axes_0, x = var_812_cast_fp16_5)[name = string("op_881_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_69_to_fp16, x = input_147_cast_fp16)[name = string("x_37_cast_fp16")]; - tensor var_884_axes_0 = const()[name = string("op_884_axes_0"), val = tensor([1])]; - tensor var_884_cast_fp16 = expand_dims(axes = var_884_axes_0, x = var_812_cast_fp16_4)[name = string("op_884_cast_fp16")]; - fp16 var_885_to_fp16 = const()[name = string("op_885_to_fp16"), val = fp16(0x1p+0)]; - tensor var_886_cast_fp16 = add(x = var_884_cast_fp16, y = var_885_to_fp16)[name = string("op_886_cast_fp16")]; - tensor var_887_cast_fp16 = mul(x = x_37_cast_fp16, y = var_886_cast_fp16)[name = string("op_887_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_812_cast_fp16_3)[name = string("op_888_cast_fp16")]; - tensor input_149_cast_fp16 = add(x = var_887_cast_fp16, y = var_888_cast_fp16)[name = string("input_149_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(27821952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28870592))))[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(28874752)))]; - tensor linear_38_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_149_cast_fp16)[name = string("linear_38_cast_fp16")]; - string input_153_mode_0 = const()[name = string("input_153_mode_0"), val = string("EXACT")]; - tensor input_153_cast_fp16 = gelu(mode = input_153_mode_0, x = linear_38_cast_fp16)[name = string("input_153_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(28878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29927552))))[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(29928640)))]; - tensor linear_39_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_153_cast_fp16)[name = string("linear_39_cast_fp16")]; - tensor var_901_cast_fp16 = mul(x = var_881_cast_fp16, y = linear_39_cast_fp16)[name = string("op_901_cast_fp16")]; - tensor input_161_cast_fp16 = add(x = input_147_cast_fp16, y = var_901_cast_fp16)[name = string("input_161_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(29929728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31502656))))[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(31508864)))]; - tensor linear_40_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_39_cast_fp16)[name = string("linear_40_cast_fp16")]; - tensor var_910_split_sizes_0 = const()[name = string("op_910_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_910_axis_0 = const()[name = string("op_910_axis_0"), val = int32(-1)]; - tensor var_910_cast_fp16_0, tensor var_910_cast_fp16_1, tensor var_910_cast_fp16_2, tensor var_910_cast_fp16_3, tensor var_910_cast_fp16_4, tensor var_910_cast_fp16_5 = split(axis = var_910_axis_0, split_sizes = var_910_split_sizes_0, x = linear_40_cast_fp16)[name = string("op_910_cast_fp16")]; - tensor var_916_axes_0 = const()[name = string("op_916_axes_0"), val = tensor([1])]; - tensor var_916_cast_fp16 = expand_dims(axes = var_916_axes_0, x = var_910_cast_fp16_2)[name = string("op_916_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_69_to_fp16, x = input_161_cast_fp16)[name = string("x_39_cast_fp16")]; - tensor var_919_axes_0 = const()[name = string("op_919_axes_0"), val = tensor([1])]; - tensor var_919_cast_fp16 = expand_dims(axes = var_919_axes_0, x = var_910_cast_fp16_1)[name = string("op_919_cast_fp16")]; - fp16 var_920_to_fp16 = const()[name = string("op_920_to_fp16"), val = fp16(0x1p+0)]; - tensor var_921_cast_fp16 = add(x = var_919_cast_fp16, y = var_920_to_fp16)[name = string("op_921_cast_fp16")]; - tensor var_922_cast_fp16 = mul(x = x_39_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_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_910_cast_fp16_0)[name = string("op_923_cast_fp16")]; - tensor x_41_cast_fp16 = add(x = var_922_cast_fp16, y = var_923_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(31515072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32301568))))[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(32304704)))]; - tensor linear_41_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_41_cast_fp16")]; - tensor var_935 = const()[name = string("op_935"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_13_cast_fp16 = reshape(shape = var_935, x = linear_41_cast_fp16)[name = string("qkv_13_cast_fp16")]; - tensor var_937_split_sizes_0 = const()[name = string("op_937_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_937_axis_0 = const()[name = string("op_937_axis_0"), val = int32(2)]; - tensor var_937_cast_fp16_0, tensor var_937_cast_fp16_1, tensor var_937_cast_fp16_2 = split(axis = var_937_axis_0, split_sizes = var_937_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_937_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_937_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_937_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_937_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; - tensor var_942_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_942_cast_fp16")]; - tensor var_944_axes_0 = const()[name = string("op_944_axes_0"), val = tensor([-1])]; - bool var_944_keep_dims_0 = const()[name = string("op_944_keep_dims_0"), val = bool(true)]; - tensor var_944_cast_fp16 = reduce_mean(axes = var_944_axes_0, keep_dims = var_944_keep_dims_0, x = var_942_cast_fp16)[name = string("op_944_cast_fp16")]; - fp16 var_945_to_fp16 = const()[name = string("op_945_to_fp16"), val = fp16(0x1p-23)]; - tensor var_946_cast_fp16 = add(x = var_944_cast_fp16, y = var_945_to_fp16)[name = string("op_946_cast_fp16")]; - fp32 var_947_epsilon_0 = const()[name = string("op_947_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_947_cast_fp16 = rsqrt(epsilon = var_947_epsilon_0, x = var_946_cast_fp16)[name = string("op_947_cast_fp16")]; - tensor var_948_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_947_cast_fp16)[name = string("op_948_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(32307840)))]; - tensor var_949_cast_fp16 = mul(x = var_948_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_949_cast_fp16")]; - tensor var_952_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_952_cast_fp16")]; - tensor var_954_axes_0 = const()[name = string("op_954_axes_0"), val = tensor([-1])]; - bool var_954_keep_dims_0 = const()[name = string("op_954_keep_dims_0"), val = bool(true)]; - tensor var_954_cast_fp16 = reduce_mean(axes = var_954_axes_0, keep_dims = var_954_keep_dims_0, x = var_952_cast_fp16)[name = string("op_954_cast_fp16")]; - fp16 var_955_to_fp16 = const()[name = string("op_955_to_fp16"), val = fp16(0x1p-23)]; - tensor var_956_cast_fp16 = add(x = var_954_cast_fp16, y = var_955_to_fp16)[name = string("op_956_cast_fp16")]; - fp32 var_957_epsilon_0 = const()[name = string("op_957_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_957_cast_fp16 = rsqrt(epsilon = var_957_epsilon_0, x = var_956_cast_fp16)[name = string("op_957_cast_fp16")]; - tensor var_958_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_957_cast_fp16)[name = string("op_958_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(32308032)))]; - tensor var_959_cast_fp16 = mul(x = var_958_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_959_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_959_cast_fp16)[name = string("transpose_131")]; - tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_949_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_970_perm_0 = const()[name = string("op_970_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_971 = const()[name = string("op_971"), val = tensor([1, 480, 512])]; - tensor var_970_cast_fp16 = transpose(perm = var_970_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; - tensor input_163_cast_fp16 = reshape(shape = var_971, x = var_970_cast_fp16)[name = string("input_163_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(32308224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32570432))))[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(32571520)))]; - tensor linear_42_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_163_cast_fp16)[name = string("linear_42_cast_fp16")]; - tensor var_977_cast_fp16 = mul(x = var_916_cast_fp16, y = linear_42_cast_fp16)[name = string("op_977_cast_fp16")]; - tensor input_167_cast_fp16 = add(x = input_161_cast_fp16, y = var_977_cast_fp16)[name = string("input_167_cast_fp16")]; - tensor var_979_axes_0 = const()[name = string("op_979_axes_0"), val = tensor([1])]; - tensor var_979_cast_fp16 = expand_dims(axes = var_979_axes_0, x = var_910_cast_fp16_5)[name = string("op_979_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_69_to_fp16, x = input_167_cast_fp16)[name = string("x_43_cast_fp16")]; - tensor var_982_axes_0 = const()[name = string("op_982_axes_0"), val = tensor([1])]; - tensor var_982_cast_fp16 = expand_dims(axes = var_982_axes_0, x = var_910_cast_fp16_4)[name = string("op_982_cast_fp16")]; - fp16 var_983_to_fp16 = const()[name = string("op_983_to_fp16"), val = fp16(0x1p+0)]; - tensor var_984_cast_fp16 = add(x = var_982_cast_fp16, y = var_983_to_fp16)[name = string("op_984_cast_fp16")]; - tensor var_985_cast_fp16 = mul(x = x_43_cast_fp16, y = var_984_cast_fp16)[name = string("op_985_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_910_cast_fp16_3)[name = string("op_986_cast_fp16")]; - tensor input_169_cast_fp16 = add(x = var_985_cast_fp16, y = var_986_cast_fp16)[name = string("input_169_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(32572608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33621248))))[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(33625408)))]; - tensor linear_43_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_169_cast_fp16)[name = string("linear_43_cast_fp16")]; - string input_173_mode_0 = const()[name = string("input_173_mode_0"), val = string("EXACT")]; - tensor input_173_cast_fp16 = gelu(mode = input_173_mode_0, x = linear_43_cast_fp16)[name = string("input_173_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(33629568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34678208))))[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(34679296)))]; - tensor linear_44_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_173_cast_fp16)[name = string("linear_44_cast_fp16")]; - tensor var_999_cast_fp16 = mul(x = var_979_cast_fp16, y = linear_44_cast_fp16)[name = string("op_999_cast_fp16")]; - tensor input_181_cast_fp16 = add(x = input_167_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_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(34680384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36253312))))[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(36259520)))]; - tensor linear_45_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_39_cast_fp16)[name = string("linear_45_cast_fp16")]; - tensor var_1008_split_sizes_0 = const()[name = string("op_1008_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1008_axis_0 = const()[name = string("op_1008_axis_0"), val = int32(-1)]; - tensor var_1008_cast_fp16_0, tensor var_1008_cast_fp16_1, tensor var_1008_cast_fp16_2, tensor var_1008_cast_fp16_3, tensor var_1008_cast_fp16_4, tensor var_1008_cast_fp16_5 = split(axis = var_1008_axis_0, split_sizes = var_1008_split_sizes_0, x = linear_45_cast_fp16)[name = string("op_1008_cast_fp16")]; - tensor var_1014_axes_0 = const()[name = string("op_1014_axes_0"), val = tensor([1])]; - tensor var_1014_cast_fp16 = expand_dims(axes = var_1014_axes_0, x = var_1008_cast_fp16_2)[name = string("op_1014_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_69_to_fp16, x = input_181_cast_fp16)[name = string("x_45_cast_fp16")]; - tensor var_1017_axes_0 = const()[name = string("op_1017_axes_0"), val = tensor([1])]; - tensor var_1017_cast_fp16 = expand_dims(axes = var_1017_axes_0, x = var_1008_cast_fp16_1)[name = string("op_1017_cast_fp16")]; - fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1019_cast_fp16 = add(x = var_1017_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; - tensor var_1020_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_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_1008_cast_fp16_0)[name = string("op_1021_cast_fp16")]; - tensor x_47_cast_fp16 = add(x = var_1020_cast_fp16, y = var_1021_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(36265728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37052224))))[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(37055360)))]; - tensor linear_46_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_46_cast_fp16")]; - tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_15_cast_fp16 = reshape(shape = var_1033, x = linear_46_cast_fp16)[name = string("qkv_15_cast_fp16")]; - tensor var_1035_split_sizes_0 = const()[name = string("op_1035_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1035_axis_0 = const()[name = string("op_1035_axis_0"), val = int32(2)]; - tensor var_1035_cast_fp16_0, tensor var_1035_cast_fp16_1, tensor var_1035_cast_fp16_2 = split(axis = var_1035_axis_0, split_sizes = var_1035_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1035_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_1035_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_1035_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_1035_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; - tensor var_1040_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1040_cast_fp16")]; - tensor var_1042_axes_0 = const()[name = string("op_1042_axes_0"), val = tensor([-1])]; - bool var_1042_keep_dims_0 = const()[name = string("op_1042_keep_dims_0"), val = bool(true)]; - tensor var_1042_cast_fp16 = reduce_mean(axes = var_1042_axes_0, keep_dims = var_1042_keep_dims_0, x = var_1040_cast_fp16)[name = string("op_1042_cast_fp16")]; - fp16 var_1043_to_fp16 = const()[name = string("op_1043_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1044_cast_fp16 = add(x = var_1042_cast_fp16, y = var_1043_to_fp16)[name = string("op_1044_cast_fp16")]; - fp32 var_1045_epsilon_0 = const()[name = string("op_1045_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1045_cast_fp16 = rsqrt(epsilon = var_1045_epsilon_0, x = var_1044_cast_fp16)[name = string("op_1045_cast_fp16")]; - tensor var_1046_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1045_cast_fp16)[name = string("op_1046_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(37058496)))]; - tensor var_1047_cast_fp16 = mul(x = var_1046_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1047_cast_fp16")]; - tensor var_1050_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1050_cast_fp16")]; - tensor var_1052_axes_0 = const()[name = string("op_1052_axes_0"), val = tensor([-1])]; - bool var_1052_keep_dims_0 = const()[name = string("op_1052_keep_dims_0"), val = bool(true)]; - tensor var_1052_cast_fp16 = reduce_mean(axes = var_1052_axes_0, keep_dims = var_1052_keep_dims_0, x = var_1050_cast_fp16)[name = string("op_1052_cast_fp16")]; - fp16 var_1053_to_fp16 = const()[name = string("op_1053_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1054_cast_fp16 = add(x = var_1052_cast_fp16, y = var_1053_to_fp16)[name = string("op_1054_cast_fp16")]; - fp32 var_1055_epsilon_0 = const()[name = string("op_1055_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1055_cast_fp16 = rsqrt(epsilon = var_1055_epsilon_0, x = var_1054_cast_fp16)[name = string("op_1055_cast_fp16")]; - tensor var_1056_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1055_cast_fp16)[name = string("op_1056_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(37058688)))]; - tensor var_1057_cast_fp16 = mul(x = var_1056_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1057_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_1057_cast_fp16)[name = string("transpose_127")]; - tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1047_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_1068_perm_0 = const()[name = string("op_1068_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1069 = const()[name = string("op_1069"), val = tensor([1, 480, 512])]; - tensor var_1068_cast_fp16 = transpose(perm = var_1068_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; - tensor input_183_cast_fp16 = reshape(shape = var_1069, x = var_1068_cast_fp16)[name = string("input_183_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(37058880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37321088))))[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(37322176)))]; - tensor linear_47_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_183_cast_fp16)[name = string("linear_47_cast_fp16")]; - tensor var_1075_cast_fp16 = mul(x = var_1014_cast_fp16, y = linear_47_cast_fp16)[name = string("op_1075_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_181_cast_fp16, y = var_1075_cast_fp16)[name = string("input_187_cast_fp16")]; - tensor var_1077_axes_0 = const()[name = string("op_1077_axes_0"), val = tensor([1])]; - tensor var_1077_cast_fp16 = expand_dims(axes = var_1077_axes_0, x = var_1008_cast_fp16_5)[name = string("op_1077_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_69_to_fp16, x = input_187_cast_fp16)[name = string("x_49_cast_fp16")]; - tensor var_1080_axes_0 = const()[name = string("op_1080_axes_0"), val = tensor([1])]; - tensor var_1080_cast_fp16 = expand_dims(axes = var_1080_axes_0, x = var_1008_cast_fp16_4)[name = string("op_1080_cast_fp16")]; - fp16 var_1081_to_fp16 = const()[name = string("op_1081_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1082_cast_fp16 = add(x = var_1080_cast_fp16, y = var_1081_to_fp16)[name = string("op_1082_cast_fp16")]; - tensor var_1083_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1082_cast_fp16)[name = string("op_1083_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_1008_cast_fp16_3)[name = string("op_1084_cast_fp16")]; - tensor input_189_cast_fp16 = add(x = var_1083_cast_fp16, y = var_1084_cast_fp16)[name = string("input_189_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(37323264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38371904))))[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(38376064)))]; - tensor linear_48_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_189_cast_fp16)[name = string("linear_48_cast_fp16")]; - string input_193_mode_0 = const()[name = string("input_193_mode_0"), val = string("EXACT")]; - tensor input_193_cast_fp16 = gelu(mode = input_193_mode_0, x = linear_48_cast_fp16)[name = string("input_193_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(38380224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39428864))))[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(39429952)))]; - tensor linear_49_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_193_cast_fp16)[name = string("linear_49_cast_fp16")]; - tensor var_1097_cast_fp16 = mul(x = var_1077_cast_fp16, y = linear_49_cast_fp16)[name = string("op_1097_cast_fp16")]; - tensor input_201_cast_fp16 = add(x = input_187_cast_fp16, y = var_1097_cast_fp16)[name = string("input_201_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(39431040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41003968))))[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(41010176)))]; - tensor linear_50_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_39_cast_fp16)[name = string("linear_50_cast_fp16")]; - tensor var_1106_split_sizes_0 = const()[name = string("op_1106_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1106_axis_0 = const()[name = string("op_1106_axis_0"), val = int32(-1)]; - tensor var_1106_cast_fp16_0, tensor var_1106_cast_fp16_1, tensor var_1106_cast_fp16_2, tensor var_1106_cast_fp16_3, tensor var_1106_cast_fp16_4, tensor var_1106_cast_fp16_5 = split(axis = var_1106_axis_0, split_sizes = var_1106_split_sizes_0, x = linear_50_cast_fp16)[name = string("op_1106_cast_fp16")]; - tensor var_1112_axes_0 = const()[name = string("op_1112_axes_0"), val = tensor([1])]; - tensor var_1112_cast_fp16 = expand_dims(axes = var_1112_axes_0, x = var_1106_cast_fp16_2)[name = string("op_1112_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_69_to_fp16, x = input_201_cast_fp16)[name = string("x_51_cast_fp16")]; - tensor var_1115_axes_0 = const()[name = string("op_1115_axes_0"), val = tensor([1])]; - tensor var_1115_cast_fp16 = expand_dims(axes = var_1115_axes_0, x = var_1106_cast_fp16_1)[name = string("op_1115_cast_fp16")]; - fp16 var_1116_to_fp16 = const()[name = string("op_1116_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1117_cast_fp16 = add(x = var_1115_cast_fp16, y = var_1116_to_fp16)[name = string("op_1117_cast_fp16")]; - tensor var_1118_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1117_cast_fp16)[name = string("op_1118_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_1106_cast_fp16_0)[name = string("op_1119_cast_fp16")]; - tensor x_53_cast_fp16 = add(x = var_1118_cast_fp16, y = var_1119_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(41016384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41802880))))[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(41806016)))]; - tensor linear_51_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_51_cast_fp16")]; - tensor var_1131 = const()[name = string("op_1131"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_17_cast_fp16 = reshape(shape = var_1131, x = linear_51_cast_fp16)[name = string("qkv_17_cast_fp16")]; - tensor var_1133_split_sizes_0 = const()[name = string("op_1133_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1133_axis_0 = const()[name = string("op_1133_axis_0"), val = int32(2)]; - tensor var_1133_cast_fp16_0, tensor var_1133_cast_fp16_1, tensor var_1133_cast_fp16_2 = split(axis = var_1133_axis_0, split_sizes = var_1133_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1133_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_1133_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_1133_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_1133_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; - tensor var_1138_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1138_cast_fp16")]; - tensor var_1140_axes_0 = const()[name = string("op_1140_axes_0"), val = tensor([-1])]; - bool var_1140_keep_dims_0 = const()[name = string("op_1140_keep_dims_0"), val = bool(true)]; - tensor var_1140_cast_fp16 = reduce_mean(axes = var_1140_axes_0, keep_dims = var_1140_keep_dims_0, x = var_1138_cast_fp16)[name = string("op_1140_cast_fp16")]; - fp16 var_1141_to_fp16 = const()[name = string("op_1141_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1142_cast_fp16 = add(x = var_1140_cast_fp16, y = var_1141_to_fp16)[name = string("op_1142_cast_fp16")]; - fp32 var_1143_epsilon_0 = const()[name = string("op_1143_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1143_cast_fp16 = rsqrt(epsilon = var_1143_epsilon_0, x = var_1142_cast_fp16)[name = string("op_1143_cast_fp16")]; - tensor var_1144_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1143_cast_fp16)[name = string("op_1144_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(41809152)))]; - tensor var_1145_cast_fp16 = mul(x = var_1144_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1145_cast_fp16")]; - tensor var_1148_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1148_cast_fp16")]; - tensor var_1150_axes_0 = const()[name = string("op_1150_axes_0"), val = tensor([-1])]; - bool var_1150_keep_dims_0 = const()[name = string("op_1150_keep_dims_0"), val = bool(true)]; - tensor var_1150_cast_fp16 = reduce_mean(axes = var_1150_axes_0, keep_dims = var_1150_keep_dims_0, x = var_1148_cast_fp16)[name = string("op_1150_cast_fp16")]; - fp16 var_1151_to_fp16 = const()[name = string("op_1151_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1152_cast_fp16 = add(x = var_1150_cast_fp16, y = var_1151_to_fp16)[name = string("op_1152_cast_fp16")]; - fp32 var_1153_epsilon_0 = const()[name = string("op_1153_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1153_cast_fp16 = rsqrt(epsilon = var_1153_epsilon_0, x = var_1152_cast_fp16)[name = string("op_1153_cast_fp16")]; - tensor var_1154_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1153_cast_fp16)[name = string("op_1154_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(41809344)))]; - tensor var_1155_cast_fp16 = mul(x = var_1154_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1155_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_1155_cast_fp16)[name = string("transpose_123")]; - tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1145_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_1166_perm_0 = const()[name = string("op_1166_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1167 = const()[name = string("op_1167"), val = tensor([1, 480, 512])]; - tensor var_1166_cast_fp16 = transpose(perm = var_1166_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; - tensor input_203_cast_fp16 = reshape(shape = var_1167, x = var_1166_cast_fp16)[name = string("input_203_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(41809536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42071744))))[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(42072832)))]; - tensor linear_52_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_203_cast_fp16)[name = string("linear_52_cast_fp16")]; - tensor var_1173_cast_fp16 = mul(x = var_1112_cast_fp16, y = linear_52_cast_fp16)[name = string("op_1173_cast_fp16")]; - tensor input_207_cast_fp16 = add(x = input_201_cast_fp16, y = var_1173_cast_fp16)[name = string("input_207_cast_fp16")]; - tensor var_1175_axes_0 = const()[name = string("op_1175_axes_0"), val = tensor([1])]; - tensor var_1175_cast_fp16 = expand_dims(axes = var_1175_axes_0, x = var_1106_cast_fp16_5)[name = string("op_1175_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_69_to_fp16, x = input_207_cast_fp16)[name = string("x_55_cast_fp16")]; - tensor var_1178_axes_0 = const()[name = string("op_1178_axes_0"), val = tensor([1])]; - tensor var_1178_cast_fp16 = expand_dims(axes = var_1178_axes_0, x = var_1106_cast_fp16_4)[name = string("op_1178_cast_fp16")]; - fp16 var_1179_to_fp16 = const()[name = string("op_1179_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1180_cast_fp16 = add(x = var_1178_cast_fp16, y = var_1179_to_fp16)[name = string("op_1180_cast_fp16")]; - tensor var_1181_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1180_cast_fp16)[name = string("op_1181_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_1106_cast_fp16_3)[name = string("op_1182_cast_fp16")]; - tensor input_209_cast_fp16 = add(x = var_1181_cast_fp16, y = var_1182_cast_fp16)[name = string("input_209_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(42073920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43122560))))[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(43126720)))]; - tensor linear_53_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_209_cast_fp16)[name = string("linear_53_cast_fp16")]; - string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("EXACT")]; - tensor input_213_cast_fp16 = gelu(mode = input_213_mode_0, x = linear_53_cast_fp16)[name = string("input_213_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(43130880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44179520))))[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(44180608)))]; - tensor linear_54_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_213_cast_fp16)[name = string("linear_54_cast_fp16")]; - tensor var_1195_cast_fp16 = mul(x = var_1175_cast_fp16, y = linear_54_cast_fp16)[name = string("op_1195_cast_fp16")]; - tensor input_221_cast_fp16 = add(x = input_207_cast_fp16, y = var_1195_cast_fp16)[name = string("input_221_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(44181696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45754624))))[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(45760832)))]; - tensor linear_55_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_39_cast_fp16)[name = string("linear_55_cast_fp16")]; - tensor var_1204_split_sizes_0 = const()[name = string("op_1204_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1204_axis_0 = const()[name = string("op_1204_axis_0"), val = int32(-1)]; - tensor var_1204_cast_fp16_0, tensor var_1204_cast_fp16_1, tensor var_1204_cast_fp16_2, tensor var_1204_cast_fp16_3, tensor var_1204_cast_fp16_4, tensor var_1204_cast_fp16_5 = split(axis = var_1204_axis_0, split_sizes = var_1204_split_sizes_0, x = linear_55_cast_fp16)[name = string("op_1204_cast_fp16")]; - tensor var_1210_axes_0 = const()[name = string("op_1210_axes_0"), val = tensor([1])]; - tensor var_1210_cast_fp16 = expand_dims(axes = var_1210_axes_0, x = var_1204_cast_fp16_2)[name = string("op_1210_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_69_to_fp16, x = input_221_cast_fp16)[name = string("x_57_cast_fp16")]; - tensor var_1213_axes_0 = const()[name = string("op_1213_axes_0"), val = tensor([1])]; - tensor var_1213_cast_fp16 = expand_dims(axes = var_1213_axes_0, x = var_1204_cast_fp16_1)[name = string("op_1213_cast_fp16")]; - fp16 var_1214_to_fp16 = const()[name = string("op_1214_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1215_cast_fp16 = add(x = var_1213_cast_fp16, y = var_1214_to_fp16)[name = string("op_1215_cast_fp16")]; - tensor var_1216_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1215_cast_fp16)[name = string("op_1216_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_1204_cast_fp16_0)[name = string("op_1217_cast_fp16")]; - tensor x_59_cast_fp16 = add(x = var_1216_cast_fp16, y = var_1217_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(45767040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46553536))))[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(46556672)))]; - tensor linear_56_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_56_cast_fp16")]; - tensor var_1229 = const()[name = string("op_1229"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_19_cast_fp16 = reshape(shape = var_1229, x = linear_56_cast_fp16)[name = string("qkv_19_cast_fp16")]; - tensor var_1231_split_sizes_0 = const()[name = string("op_1231_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1231_axis_0 = const()[name = string("op_1231_axis_0"), val = int32(2)]; - tensor var_1231_cast_fp16_0, tensor var_1231_cast_fp16_1, tensor var_1231_cast_fp16_2 = split(axis = var_1231_axis_0, split_sizes = var_1231_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1231_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_1231_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_1231_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_1231_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; - tensor var_1236_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1236_cast_fp16")]; - tensor var_1238_axes_0 = const()[name = string("op_1238_axes_0"), val = tensor([-1])]; - bool var_1238_keep_dims_0 = const()[name = string("op_1238_keep_dims_0"), val = bool(true)]; - tensor var_1238_cast_fp16 = reduce_mean(axes = var_1238_axes_0, keep_dims = var_1238_keep_dims_0, x = var_1236_cast_fp16)[name = string("op_1238_cast_fp16")]; - fp16 var_1239_to_fp16 = const()[name = string("op_1239_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1240_cast_fp16 = add(x = var_1238_cast_fp16, y = var_1239_to_fp16)[name = string("op_1240_cast_fp16")]; - fp32 var_1241_epsilon_0 = const()[name = string("op_1241_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1241_cast_fp16 = rsqrt(epsilon = var_1241_epsilon_0, x = var_1240_cast_fp16)[name = string("op_1241_cast_fp16")]; - tensor var_1242_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1241_cast_fp16)[name = string("op_1242_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(46559808)))]; - tensor var_1243_cast_fp16 = mul(x = var_1242_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1243_cast_fp16")]; - tensor var_1246_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1246_cast_fp16")]; - tensor var_1248_axes_0 = const()[name = string("op_1248_axes_0"), val = tensor([-1])]; - bool var_1248_keep_dims_0 = const()[name = string("op_1248_keep_dims_0"), val = bool(true)]; - tensor var_1248_cast_fp16 = reduce_mean(axes = var_1248_axes_0, keep_dims = var_1248_keep_dims_0, x = var_1246_cast_fp16)[name = string("op_1248_cast_fp16")]; - fp16 var_1249_to_fp16 = const()[name = string("op_1249_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1250_cast_fp16 = add(x = var_1248_cast_fp16, y = var_1249_to_fp16)[name = string("op_1250_cast_fp16")]; - fp32 var_1251_epsilon_0 = const()[name = string("op_1251_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1251_cast_fp16 = rsqrt(epsilon = var_1251_epsilon_0, x = var_1250_cast_fp16)[name = string("op_1251_cast_fp16")]; - tensor var_1252_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1251_cast_fp16)[name = string("op_1252_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(46560000)))]; - tensor var_1253_cast_fp16 = mul(x = var_1252_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1253_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_1253_cast_fp16)[name = string("transpose_119")]; - tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1243_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_1264_perm_0 = const()[name = string("op_1264_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1265 = const()[name = string("op_1265"), val = tensor([1, 480, 512])]; - tensor var_1264_cast_fp16 = transpose(perm = var_1264_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; - tensor input_223_cast_fp16 = reshape(shape = var_1265, x = var_1264_cast_fp16)[name = string("input_223_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(46560192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46822400))))[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(46823488)))]; - tensor linear_57_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_223_cast_fp16)[name = string("linear_57_cast_fp16")]; - tensor var_1271_cast_fp16 = mul(x = var_1210_cast_fp16, y = linear_57_cast_fp16)[name = string("op_1271_cast_fp16")]; - tensor input_227_cast_fp16 = add(x = input_221_cast_fp16, y = var_1271_cast_fp16)[name = string("input_227_cast_fp16")]; - tensor var_1273_axes_0 = const()[name = string("op_1273_axes_0"), val = tensor([1])]; - tensor var_1273_cast_fp16 = expand_dims(axes = var_1273_axes_0, x = var_1204_cast_fp16_5)[name = string("op_1273_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_69_to_fp16, x = input_227_cast_fp16)[name = string("x_61_cast_fp16")]; - tensor var_1276_axes_0 = const()[name = string("op_1276_axes_0"), val = tensor([1])]; - tensor var_1276_cast_fp16 = expand_dims(axes = var_1276_axes_0, x = var_1204_cast_fp16_4)[name = string("op_1276_cast_fp16")]; - fp16 var_1277_to_fp16 = const()[name = string("op_1277_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1278_cast_fp16 = add(x = var_1276_cast_fp16, y = var_1277_to_fp16)[name = string("op_1278_cast_fp16")]; - tensor var_1279_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1278_cast_fp16)[name = string("op_1279_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_1204_cast_fp16_3)[name = string("op_1280_cast_fp16")]; - tensor input_229_cast_fp16 = add(x = var_1279_cast_fp16, y = var_1280_cast_fp16)[name = string("input_229_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(46824576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47873216))))[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(47877376)))]; - tensor linear_58_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_229_cast_fp16)[name = string("linear_58_cast_fp16")]; - string input_233_mode_0 = const()[name = string("input_233_mode_0"), val = string("EXACT")]; - tensor input_233_cast_fp16 = gelu(mode = input_233_mode_0, x = linear_58_cast_fp16)[name = string("input_233_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(47881536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48930176))))[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(48931264)))]; - tensor linear_59_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_233_cast_fp16)[name = string("linear_59_cast_fp16")]; - tensor var_1293_cast_fp16 = mul(x = var_1273_cast_fp16, y = linear_59_cast_fp16)[name = string("op_1293_cast_fp16")]; - tensor input_241_cast_fp16 = add(x = input_227_cast_fp16, y = var_1293_cast_fp16)[name = string("input_241_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(48932352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50505280))))[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(50511488)))]; - tensor linear_60_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_39_cast_fp16)[name = string("linear_60_cast_fp16")]; - tensor var_1302_split_sizes_0 = const()[name = string("op_1302_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1302_axis_0 = const()[name = string("op_1302_axis_0"), val = int32(-1)]; - tensor var_1302_cast_fp16_0, tensor var_1302_cast_fp16_1, tensor var_1302_cast_fp16_2, tensor var_1302_cast_fp16_3, tensor var_1302_cast_fp16_4, tensor var_1302_cast_fp16_5 = split(axis = var_1302_axis_0, split_sizes = var_1302_split_sizes_0, x = linear_60_cast_fp16)[name = string("op_1302_cast_fp16")]; - tensor var_1308_axes_0 = const()[name = string("op_1308_axes_0"), val = tensor([1])]; - tensor var_1308_cast_fp16 = expand_dims(axes = var_1308_axes_0, x = var_1302_cast_fp16_2)[name = string("op_1308_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_69_to_fp16, x = input_241_cast_fp16)[name = string("x_63_cast_fp16")]; - tensor var_1311_axes_0 = const()[name = string("op_1311_axes_0"), val = tensor([1])]; - tensor var_1311_cast_fp16 = expand_dims(axes = var_1311_axes_0, x = var_1302_cast_fp16_1)[name = string("op_1311_cast_fp16")]; - fp16 var_1312_to_fp16 = const()[name = string("op_1312_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1313_cast_fp16 = add(x = var_1311_cast_fp16, y = var_1312_to_fp16)[name = string("op_1313_cast_fp16")]; - tensor var_1314_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1313_cast_fp16)[name = string("op_1314_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_1302_cast_fp16_0)[name = string("op_1315_cast_fp16")]; - tensor x_65_cast_fp16 = add(x = var_1314_cast_fp16, y = var_1315_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(50517696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51304192))))[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(51307328)))]; - tensor linear_61_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_61_cast_fp16")]; - tensor var_1327 = const()[name = string("op_1327"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_21_cast_fp16 = reshape(shape = var_1327, x = linear_61_cast_fp16)[name = string("qkv_21_cast_fp16")]; - tensor var_1329_split_sizes_0 = const()[name = string("op_1329_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1329_axis_0 = const()[name = string("op_1329_axis_0"), val = int32(2)]; - tensor var_1329_cast_fp16_0, tensor var_1329_cast_fp16_1, tensor var_1329_cast_fp16_2 = split(axis = var_1329_axis_0, split_sizes = var_1329_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1329_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_1329_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_1329_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_1329_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; - tensor var_1334_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1334_cast_fp16")]; - tensor var_1336_axes_0 = const()[name = string("op_1336_axes_0"), val = tensor([-1])]; - bool var_1336_keep_dims_0 = const()[name = string("op_1336_keep_dims_0"), val = bool(true)]; - tensor var_1336_cast_fp16 = reduce_mean(axes = var_1336_axes_0, keep_dims = var_1336_keep_dims_0, x = var_1334_cast_fp16)[name = string("op_1336_cast_fp16")]; - fp16 var_1337_to_fp16 = const()[name = string("op_1337_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1338_cast_fp16 = add(x = var_1336_cast_fp16, y = var_1337_to_fp16)[name = string("op_1338_cast_fp16")]; - fp32 var_1339_epsilon_0 = const()[name = string("op_1339_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1339_cast_fp16 = rsqrt(epsilon = var_1339_epsilon_0, x = var_1338_cast_fp16)[name = string("op_1339_cast_fp16")]; - tensor var_1340_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1339_cast_fp16)[name = string("op_1340_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(51310464)))]; - tensor var_1341_cast_fp16 = mul(x = var_1340_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1341_cast_fp16")]; - tensor var_1344_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1344_cast_fp16")]; - tensor var_1346_axes_0 = const()[name = string("op_1346_axes_0"), val = tensor([-1])]; - bool var_1346_keep_dims_0 = const()[name = string("op_1346_keep_dims_0"), val = bool(true)]; - tensor var_1346_cast_fp16 = reduce_mean(axes = var_1346_axes_0, keep_dims = var_1346_keep_dims_0, x = var_1344_cast_fp16)[name = string("op_1346_cast_fp16")]; - fp16 var_1347_to_fp16 = const()[name = string("op_1347_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1348_cast_fp16 = add(x = var_1346_cast_fp16, y = var_1347_to_fp16)[name = string("op_1348_cast_fp16")]; - fp32 var_1349_epsilon_0 = const()[name = string("op_1349_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1349_cast_fp16 = rsqrt(epsilon = var_1349_epsilon_0, x = var_1348_cast_fp16)[name = string("op_1349_cast_fp16")]; - tensor var_1350_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1349_cast_fp16)[name = string("op_1350_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(51310656)))]; - tensor var_1351_cast_fp16 = mul(x = var_1350_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1351_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_1351_cast_fp16)[name = string("transpose_115")]; - tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1341_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_1362_perm_0 = const()[name = string("op_1362_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1363 = const()[name = string("op_1363"), val = tensor([1, 480, 512])]; - tensor var_1362_cast_fp16 = transpose(perm = var_1362_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; - tensor input_243_cast_fp16 = reshape(shape = var_1363, x = var_1362_cast_fp16)[name = string("input_243_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(51310848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51573056))))[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(51574144)))]; - tensor linear_62_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_243_cast_fp16)[name = string("linear_62_cast_fp16")]; - tensor var_1369_cast_fp16 = mul(x = var_1308_cast_fp16, y = linear_62_cast_fp16)[name = string("op_1369_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_241_cast_fp16, y = var_1369_cast_fp16)[name = string("input_247_cast_fp16")]; - tensor var_1371_axes_0 = const()[name = string("op_1371_axes_0"), val = tensor([1])]; - tensor var_1371_cast_fp16 = expand_dims(axes = var_1371_axes_0, x = var_1302_cast_fp16_5)[name = string("op_1371_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_69_to_fp16, x = input_247_cast_fp16)[name = string("x_67_cast_fp16")]; - tensor var_1374_axes_0 = const()[name = string("op_1374_axes_0"), val = tensor([1])]; - tensor var_1374_cast_fp16 = expand_dims(axes = var_1374_axes_0, x = var_1302_cast_fp16_4)[name = string("op_1374_cast_fp16")]; - fp16 var_1375_to_fp16 = const()[name = string("op_1375_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1376_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1375_to_fp16)[name = string("op_1376_cast_fp16")]; - tensor var_1377_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1376_cast_fp16)[name = string("op_1377_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_1302_cast_fp16_3)[name = string("op_1378_cast_fp16")]; - tensor input_249_cast_fp16 = add(x = var_1377_cast_fp16, y = var_1378_cast_fp16)[name = string("input_249_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(51575232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52623872))))[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(52628032)))]; - tensor linear_63_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_249_cast_fp16)[name = string("linear_63_cast_fp16")]; - string input_253_mode_0 = const()[name = string("input_253_mode_0"), val = string("EXACT")]; - tensor input_253_cast_fp16 = gelu(mode = input_253_mode_0, x = linear_63_cast_fp16)[name = string("input_253_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(52632192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53680832))))[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(53681920)))]; - tensor linear_64_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_253_cast_fp16)[name = string("linear_64_cast_fp16")]; - tensor var_1391_cast_fp16 = mul(x = var_1371_cast_fp16, y = linear_64_cast_fp16)[name = string("op_1391_cast_fp16")]; - tensor input_261_cast_fp16 = add(x = input_247_cast_fp16, y = var_1391_cast_fp16)[name = string("input_261_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(53683008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55255936))))[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(55262144)))]; - tensor linear_65_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_39_cast_fp16)[name = string("linear_65_cast_fp16")]; - tensor var_1400_split_sizes_0 = const()[name = string("op_1400_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1400_axis_0 = const()[name = string("op_1400_axis_0"), val = int32(-1)]; - tensor var_1400_cast_fp16_0, tensor var_1400_cast_fp16_1, tensor var_1400_cast_fp16_2, tensor var_1400_cast_fp16_3, tensor var_1400_cast_fp16_4, tensor var_1400_cast_fp16_5 = split(axis = var_1400_axis_0, split_sizes = var_1400_split_sizes_0, x = linear_65_cast_fp16)[name = string("op_1400_cast_fp16")]; - tensor var_1406_axes_0 = const()[name = string("op_1406_axes_0"), val = tensor([1])]; - tensor var_1406_cast_fp16 = expand_dims(axes = var_1406_axes_0, x = var_1400_cast_fp16_2)[name = string("op_1406_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_69_to_fp16, x = input_261_cast_fp16)[name = string("x_69_cast_fp16")]; - tensor var_1409_axes_0 = const()[name = string("op_1409_axes_0"), val = tensor([1])]; - tensor var_1409_cast_fp16 = expand_dims(axes = var_1409_axes_0, x = var_1400_cast_fp16_1)[name = string("op_1409_cast_fp16")]; - fp16 var_1410_to_fp16 = const()[name = string("op_1410_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1411_cast_fp16 = add(x = var_1409_cast_fp16, y = var_1410_to_fp16)[name = string("op_1411_cast_fp16")]; - tensor var_1412_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1411_cast_fp16)[name = string("op_1412_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_1400_cast_fp16_0)[name = string("op_1413_cast_fp16")]; - tensor x_71_cast_fp16 = add(x = var_1412_cast_fp16, y = var_1413_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(55268352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56054848))))[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(56057984)))]; - tensor linear_66_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_66_cast_fp16")]; - tensor var_1425 = const()[name = string("op_1425"), val = tensor([1, 480, 3, 8, 64])]; - tensor qkv_23_cast_fp16 = reshape(shape = var_1425, x = linear_66_cast_fp16)[name = string("qkv_23_cast_fp16")]; - tensor var_1427_split_sizes_0 = const()[name = string("op_1427_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1427_axis_0 = const()[name = string("op_1427_axis_0"), val = int32(2)]; - tensor var_1427_cast_fp16_0, tensor var_1427_cast_fp16_1, tensor var_1427_cast_fp16_2 = split(axis = var_1427_axis_0, split_sizes = var_1427_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1427_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_1427_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_1427_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_1427_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; - tensor var_1432_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1432_cast_fp16")]; - tensor var_1434_axes_0 = const()[name = string("op_1434_axes_0"), val = tensor([-1])]; - bool var_1434_keep_dims_0 = const()[name = string("op_1434_keep_dims_0"), val = bool(true)]; - tensor var_1434_cast_fp16 = reduce_mean(axes = var_1434_axes_0, keep_dims = var_1434_keep_dims_0, x = var_1432_cast_fp16)[name = string("op_1434_cast_fp16")]; - fp16 var_1435_to_fp16 = const()[name = string("op_1435_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1436_cast_fp16 = add(x = var_1434_cast_fp16, y = var_1435_to_fp16)[name = string("op_1436_cast_fp16")]; - fp32 var_1437_epsilon_0 = const()[name = string("op_1437_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1437_cast_fp16 = rsqrt(epsilon = var_1437_epsilon_0, x = var_1436_cast_fp16)[name = string("op_1437_cast_fp16")]; - tensor var_1438_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1437_cast_fp16)[name = string("op_1438_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(56061120)))]; - tensor var_1439_cast_fp16 = mul(x = var_1438_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1439_cast_fp16")]; - tensor var_1442_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1442_cast_fp16")]; - tensor var_1444_axes_0 = const()[name = string("op_1444_axes_0"), val = tensor([-1])]; - bool var_1444_keep_dims_0 = const()[name = string("op_1444_keep_dims_0"), val = bool(true)]; - tensor var_1444_cast_fp16 = reduce_mean(axes = var_1444_axes_0, keep_dims = var_1444_keep_dims_0, x = var_1442_cast_fp16)[name = string("op_1444_cast_fp16")]; - fp16 var_1445_to_fp16 = const()[name = string("op_1445_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1446_cast_fp16 = add(x = var_1444_cast_fp16, y = var_1445_to_fp16)[name = string("op_1446_cast_fp16")]; - fp32 var_1447_epsilon_0 = const()[name = string("op_1447_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1447_cast_fp16 = rsqrt(epsilon = var_1447_epsilon_0, x = var_1446_cast_fp16)[name = string("op_1447_cast_fp16")]; - tensor var_1448_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1447_cast_fp16)[name = string("op_1448_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(56061312)))]; - tensor var_1449_cast_fp16 = mul(x = var_1448_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1449_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_1449_cast_fp16)[name = string("transpose_111")]; - tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1439_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_1460_perm_0 = const()[name = string("op_1460_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1461 = const()[name = string("op_1461"), val = tensor([1, 480, 512])]; - tensor var_1460_cast_fp16 = transpose(perm = var_1460_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; - tensor input_263_cast_fp16 = reshape(shape = var_1461, x = var_1460_cast_fp16)[name = string("input_263_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(56061504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56323712))))[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(56324800)))]; - tensor linear_67_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_263_cast_fp16)[name = string("linear_67_cast_fp16")]; - tensor var_1467_cast_fp16 = mul(x = var_1406_cast_fp16, y = linear_67_cast_fp16)[name = string("op_1467_cast_fp16")]; - tensor input_267_cast_fp16 = add(x = input_261_cast_fp16, y = var_1467_cast_fp16)[name = string("input_267_cast_fp16")]; - tensor var_1469_axes_0 = const()[name = string("op_1469_axes_0"), val = tensor([1])]; - tensor var_1469_cast_fp16 = expand_dims(axes = var_1469_axes_0, x = var_1400_cast_fp16_5)[name = string("op_1469_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_69_to_fp16, x = input_267_cast_fp16)[name = string("x_73_cast_fp16")]; - tensor var_1472_axes_0 = const()[name = string("op_1472_axes_0"), val = tensor([1])]; - tensor var_1472_cast_fp16 = expand_dims(axes = var_1472_axes_0, x = var_1400_cast_fp16_4)[name = string("op_1472_cast_fp16")]; - fp16 var_1473_to_fp16 = const()[name = string("op_1473_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1474_cast_fp16 = add(x = var_1472_cast_fp16, y = var_1473_to_fp16)[name = string("op_1474_cast_fp16")]; - tensor var_1475_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1474_cast_fp16)[name = string("op_1475_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_1400_cast_fp16_3)[name = string("op_1476_cast_fp16")]; - tensor input_269_cast_fp16 = add(x = var_1475_cast_fp16, y = var_1476_cast_fp16)[name = string("input_269_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(56325888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57374528))))[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(57378688)))]; - tensor linear_68_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_269_cast_fp16)[name = string("linear_68_cast_fp16")]; - string input_273_mode_0 = const()[name = string("input_273_mode_0"), val = string("EXACT")]; - tensor input_273_cast_fp16 = gelu(mode = input_273_mode_0, x = linear_68_cast_fp16)[name = string("input_273_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(57382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58431488))))[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(58432576)))]; - tensor linear_69_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_273_cast_fp16)[name = string("linear_69_cast_fp16")]; - tensor var_1489_cast_fp16 = mul(x = var_1469_cast_fp16, y = linear_69_cast_fp16)[name = string("op_1489_cast_fp16")]; - tensor input_281_cast_fp16 = add(x = input_267_cast_fp16, y = var_1489_cast_fp16)[name = string("input_281_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(58433664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58958016))))[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(58960128)))]; - tensor linear_70_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_39_cast_fp16)[name = string("linear_70_cast_fp16")]; - tensor var_1495_split_sizes_0 = const()[name = string("op_1495_split_sizes_0"), val = tensor([512, 512])]; - int32 var_1495_axis_0 = const()[name = string("op_1495_axis_0"), val = int32(-1)]; - tensor var_1495_cast_fp16_0, tensor var_1495_cast_fp16_1 = split(axis = var_1495_axis_0, split_sizes = var_1495_split_sizes_0, x = linear_70_cast_fp16)[name = string("op_1495_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_69_to_fp16, x = input_281_cast_fp16)[name = string("x_cast_fp16")]; - tensor var_1499_axes_0 = const()[name = string("op_1499_axes_0"), val = tensor([1])]; - tensor var_1499_cast_fp16 = expand_dims(axes = var_1499_axes_0, x = var_1495_cast_fp16_1)[name = string("op_1499_cast_fp16")]; - fp16 var_1500_to_fp16 = const()[name = string("op_1500_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1501_cast_fp16 = add(x = var_1499_cast_fp16, y = var_1500_to_fp16)[name = string("op_1501_cast_fp16")]; - tensor var_1502_cast_fp16 = mul(x = x_cast_fp16, y = var_1501_cast_fp16)[name = string("op_1502_cast_fp16")]; - tensor var_1503_axes_0 = const()[name = string("op_1503_axes_0"), val = tensor([1])]; - tensor var_1503_cast_fp16 = expand_dims(axes = var_1503_axes_0, x = var_1495_cast_fp16_0)[name = string("op_1503_cast_fp16")]; - tensor encoded_cast_fp16 = add(x = var_1502_cast_fp16, y = var_1503_cast_fp16)[name = string("encoded_cast_fp16")]; - tensor input_283_begin_0 = const()[name = string("input_283_begin_0"), val = tensor([0, 96, 0])]; - tensor input_283_end_0 = const()[name = string("input_283_end_0"), val = tensor([1, 480, 512])]; - tensor input_283_end_mask_0 = const()[name = string("input_283_end_mask_0"), val = tensor([true, true, true])]; - tensor input_283_cast_fp16 = slice_by_index(begin = input_283_begin_0, end = input_283_end_0, end_mask = input_283_end_mask_0, x = encoded_cast_fp16)[name = string("input_283_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(58962240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59224448))))[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(59225536)))]; - tensor linear_71_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_71_cast_fp16")]; - string input_287_mode_0 = const()[name = string("input_287_mode_0"), val = string("EXACT")]; - tensor input_287_cast_fp16 = gelu(mode = input_287_mode_0, x = linear_71_cast_fp16)[name = string("input_287_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(59226624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59238976))))[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(59239104)))]; - tensor linear_72_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_72_cast_fp16")]; - tensor var_1517_perm_0 = const()[name = string("op_1517_perm_0"), val = tensor([0, 2, 1])]; - string var_1517_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1517_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; - tensor var_1517_cast_fp16 = transpose(perm = var_1517_perm_0, x = linear_72_cast_fp16)[name = string("transpose_108")]; - tensor velocity = cast(dtype = var_1517_cast_fp16_to_fp32_dtype_0, x = var_1517_cast_fp16)[name = string("cast_1")]; - } -> (velocity); - func vector_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 prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { - int32 var_33 = const()[name = string("op_33"), val = int32(0)]; - tensor present = not_equal(x = delivery_present, y = var_33)[name = string("present")]; - int32 var_35 = const()[name = string("op_35"), val = int32(0)]; - tensor latent_mask_1 = not_equal(x = latent_mask, y = var_35)[name = string("latent_mask")]; - int32 var_37 = const()[name = string("op_37"), val = int32(0)]; - tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_37)[name = string("prefix_mask_1")]; - int32 var_58 = const()[name = string("op_58"), val = int32(-1)]; - int32 var_73 = const()[name = string("op_73"), val = int32(1)]; - int32 var_87 = const()[name = string("op_87"), val = int32(7)]; - tensor var_88 = mul(x = voice_id, y = var_87)[name = string("op_88")]; - tensor index = add(x = var_88, 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(35968))))[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_20")]; - tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_19")]; - 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(70)]; - 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_18")]; - tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_17")]; - 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(70)]; - 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(36224)))]; - 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_16")]; - 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(40768)))]; - 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(45312)))]; - 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(49856)))]; - 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(50112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62464))))[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(63552)))]; - tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_15")]; - 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_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_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109248))))[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_14")]; - tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; - tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; - int32 var_150_axis_0 = const()[name = string("op_150_axis_0"), val = int32(0)]; - int32 var_150_batch_dims_0 = const()[name = string("op_150_batch_dims_0"), val = int32(0)]; - bool var_150_validate_indices_0 = const()[name = string("op_150_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(109504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154624))))[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_13")]; - tensor var_150_cast_fp16_cast_uint16 = gather(axis = var_150_axis_0, batch_dims = var_150_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_150_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_150_cast_fp16_cast_uint16")]; - int32 var_155_axis_0 = const()[name = string("op_155_axis_0"), val = int32(0)]; - int32 var_155_batch_dims_0 = const()[name = string("op_155_batch_dims_0"), val = int32(0)]; - bool var_155_validate_indices_0 = const()[name = string("op_155_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(154880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187712))))[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_12")]; - tensor var_155_cast_fp16_cast_uint16 = gather(axis = var_155_axis_0, batch_dims = var_155_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_155_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_155_cast_fp16_cast_uint16")]; - tensor hidden_7_cast_fp16 = add(x = var_150_cast_fp16_cast_uint16, y = var_155_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_10")]; - 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_11")]; - 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(187904)))]; - 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(190016)))]; - 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_166_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_166_cast_fp16")]; - tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_166_cast_fp16)[name = string("hidden_11_cast_fp16")]; - int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(0)]; - int32 var_170_batch_dims_0 = const()[name = string("op_170_batch_dims_0"), val = int32(0)]; - bool var_170_validate_indices_0 = const()[name = string("op_170_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(191104)))]; - 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_9")]; - tensor var_170_cast_fp16_cast_uint16 = gather(axis = var_170_axis_0, batch_dims = var_170_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_170_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_170_cast_fp16_cast_uint16")]; - tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_170_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; - tensor op_174_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59568832))), scale = tensor([[[0x1.46cp-5]]]))[name = string("op_174_to_fp16_quantized")]; - tensor hidden_17_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_174_to_fp16_quantized)[name = string("hidden_17_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(261888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267072))))[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(267200)))]; - 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_186_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_186_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_58, interleave = input_15_interleave_0, values = (var_186_cast_fp16, present_to_fp16))[name = string("input_15_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(274432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279616))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; - 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(280704)))]; - tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; - tensor var_204_axes_0 = const()[name = string("op_204_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_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = time_to_fp16)[name = string("op_204_cast_fp16")]; - tensor var_205_to_fp16 = const()[name = string("op_205_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281792)))]; - tensor phases_1_cast_fp16 = mul(x = var_204_cast_fp16, y = var_205_to_fp16)[name = string("phases_1_cast_fp16")]; - fp16 var_207_to_fp16 = const()[name = string("op_207_to_fp16"), val = fp16(0x1.92p+2)]; - tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_207_to_fp16)[name = string("phases_cast_fp16")]; - tensor var_209_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_209_cast_fp16")]; - tensor var_210_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_210_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_58, interleave = input_21_interleave_0, values = (var_209_cast_fp16, var_210_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(282368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(544576))))[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(545664)))]; - 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(546752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808960))))[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(810048)))]; - 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_220_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_220_cast_fp16")]; - tensor var_221_cast_fp16 = add(x = var_220_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_221_cast_fp16")]; - tensor var_222_cast_fp16 = add(x = var_221_cast_fp16, y = linear_2_cast_fp16)[name = string("op_222_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(811136)))]; - tensor cond_1_cast_fp16 = add(x = var_222_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; - fp16 var_61_to_fp16 = const()[name = string("op_61_to_fp16"), val = fp16(0x0p+0)]; - fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x1p+0)]; - tensor clip_0_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; - tensor clip_1_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; - tensor var_236_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_236_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(812224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(828672))))[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(829760)))]; - 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_236_cast_fp16)[name = string("linear_5_cast_fp16")]; - tensor var_242_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_242_cast_fp16")]; - tensor var_243_axes_0 = const()[name = string("op_243_axes_0"), val = tensor([-1])]; - tensor var_243_cast_fp16 = expand_dims(axes = var_243_axes_0, x = clip_1_cast_fp16)[name = string("op_243_cast_fp16")]; - tensor var_244_cast_fp16 = mul(x = var_242_cast_fp16, y = var_243_cast_fp16)[name = string("op_244_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(830848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(847296))))[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_244_cast_fp16)[name = string("linear_6_cast_fp16")]; - tensor var_250_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_250_cast_fp16")]; - tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_250_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_260_axes_0 = const()[name = string("op_260_axes_0"), val = tensor([-1])]; - tensor var_260_cast_fp16 = expand_dims(axes = var_260_axes_0, x = mask_7_cast_fp16)[name = string("op_260_cast_fp16")]; - tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_260_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(848384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110592))))[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(1111680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1373888))))[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_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_37_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_37_cast_fp16")]; - tensor input_33_perm_0 = const()[name = string("input_33_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_33_cast_fp16 = transpose(perm = input_33_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; - tensor linear_9_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_9_cast_fp16")]; - tensor op_277_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1374976))), scale = tensor([[[0x1.278p-5]]]))[name = string("op_277_to_fp16_quantized")]; - tensor context_3_cast_fp16 = add(x = linear_9_cast_fp16, y = op_277_to_fp16_quantized)[name = string("context_3_cast_fp16")]; - tensor var_280_to_fp16 = const()[name = string("op_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1424192)))]; - tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_280_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_73, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_17_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_73, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; - fp16 var_288_to_fp16 = const()[name = string("op_288_to_fp16"), val = fp16(0x1p-3)]; - tensor input_41_cast_fp16 = mul(x = x_1_cast_fp16, y = var_288_to_fp16)[name = string("input_41_cast_fp16")]; - tensor input_39_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("input_39_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(1425280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2998208))))[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(3004416)))]; - tensor linear_10_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_39_cast_fp16)[name = string("linear_10_cast_fp16")]; - tensor var_322_split_sizes_0 = const()[name = string("op_322_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_322_axis_0 = const()[name = string("op_322_axis_0"), val = int32(-1)]; - tensor var_322_cast_fp16_0, tensor var_322_cast_fp16_1, tensor var_322_cast_fp16_2, tensor var_322_cast_fp16_3, tensor var_322_cast_fp16_4, tensor var_322_cast_fp16_5 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = linear_10_cast_fp16)[name = string("op_322_cast_fp16")]; - tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; - tensor var_328_cast_fp16 = expand_dims(axes = var_328_axes_0, x = var_322_cast_fp16_2)[name = string("op_328_cast_fp16")]; - tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; - fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x1.8p-23)]; - tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_69_to_fp16, x = input_41_cast_fp16)[name = string("x_3_cast_fp16")]; - tensor var_331_axes_0 = const()[name = string("op_331_axes_0"), val = tensor([1])]; - tensor var_331_cast_fp16 = expand_dims(axes = var_331_axes_0, x = var_322_cast_fp16_1)[name = string("op_331_cast_fp16")]; - fp16 var_332_to_fp16 = const()[name = string("op_332_to_fp16"), val = fp16(0x1p+0)]; - tensor var_333_cast_fp16 = add(x = var_331_cast_fp16, y = var_332_to_fp16)[name = string("op_333_cast_fp16")]; - tensor var_334_cast_fp16 = mul(x = x_3_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_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_322_cast_fp16_0)[name = string("op_335_cast_fp16")]; - tensor x_5_cast_fp16 = add(x = var_334_cast_fp16, y = var_335_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(3010624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3797120))))[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(3800256)))]; - tensor linear_11_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_11_cast_fp16")]; - tensor var_347 = const()[name = string("op_347"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_1_cast_fp16 = reshape(shape = var_347, x = linear_11_cast_fp16)[name = string("qkv_1_cast_fp16")]; - tensor var_349_split_sizes_0 = const()[name = string("op_349_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_349_axis_0 = const()[name = string("op_349_axis_0"), val = int32(2)]; - tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1, tensor var_349_cast_fp16_2 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_349_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_349_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_349_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_349_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; - tensor var_354_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_354_cast_fp16")]; - tensor var_356_axes_0 = const()[name = string("op_356_axes_0"), val = tensor([-1])]; - bool var_356_keep_dims_0 = const()[name = string("op_356_keep_dims_0"), val = bool(true)]; - tensor var_356_cast_fp16 = reduce_mean(axes = var_356_axes_0, keep_dims = var_356_keep_dims_0, x = var_354_cast_fp16)[name = string("op_356_cast_fp16")]; - fp16 var_357_to_fp16 = const()[name = string("op_357_to_fp16"), val = fp16(0x1p-23)]; - tensor var_358_cast_fp16 = add(x = var_356_cast_fp16, y = var_357_to_fp16)[name = string("op_358_cast_fp16")]; - fp32 var_359_epsilon_0 = const()[name = string("op_359_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_359_cast_fp16 = rsqrt(epsilon = var_359_epsilon_0, x = var_358_cast_fp16)[name = string("op_359_cast_fp16")]; - tensor var_360_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_359_cast_fp16)[name = string("op_360_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(3803392)))]; - tensor var_361_cast_fp16 = mul(x = var_360_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_361_cast_fp16")]; - tensor var_364_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_364_cast_fp16")]; - tensor var_366_axes_0 = const()[name = string("op_366_axes_0"), val = tensor([-1])]; - bool var_366_keep_dims_0 = const()[name = string("op_366_keep_dims_0"), val = bool(true)]; - tensor var_366_cast_fp16 = reduce_mean(axes = var_366_axes_0, keep_dims = var_366_keep_dims_0, x = var_364_cast_fp16)[name = string("op_366_cast_fp16")]; - fp16 var_367_to_fp16 = const()[name = string("op_367_to_fp16"), val = fp16(0x1p-23)]; - tensor var_368_cast_fp16 = add(x = var_366_cast_fp16, y = var_367_to_fp16)[name = string("op_368_cast_fp16")]; - fp32 var_369_epsilon_0 = const()[name = string("op_369_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_369_cast_fp16 = rsqrt(epsilon = var_369_epsilon_0, x = var_368_cast_fp16)[name = string("op_369_cast_fp16")]; - tensor var_370_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_369_cast_fp16)[name = string("op_370_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(3803584)))]; - tensor var_371_cast_fp16 = mul(x = var_370_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_371_cast_fp16")]; - tensor var_376 = const()[name = string("op_376"), val = tensor([1, 1, 1, 608])]; - tensor var_377 = reshape(shape = var_376, x = valid_mask)[name = string("op_377")]; - tensor var_379 = logical_not(x = var_377)[name = string("op_379")]; - fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(-inf)]; - tensor var_375_to_fp16 = const()[name = string("op_375_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59831040)))]; - tensor attn_mask_1_cast_fp16 = select(a = var_68_to_fp16, b = var_375_to_fp16, cond = var_379)[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_371_cast_fp16)[name = string("transpose_155")]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_361_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_382_perm_0 = const()[name = string("op_382_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_383 = const()[name = string("op_383"), val = tensor([1, 608, 512])]; - tensor var_382_cast_fp16 = transpose(perm = var_382_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; - tensor input_43_cast_fp16 = reshape(shape = var_383, x = var_382_cast_fp16)[name = string("input_43_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(3804288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4066496))))[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(4067584)))]; - tensor linear_12_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_43_cast_fp16)[name = string("linear_12_cast_fp16")]; - tensor var_389_cast_fp16 = mul(x = var_328_cast_fp16, y = linear_12_cast_fp16)[name = string("op_389_cast_fp16")]; - tensor input_47_cast_fp16 = add(x = input_41_cast_fp16, y = var_389_cast_fp16)[name = string("input_47_cast_fp16")]; - tensor var_391_axes_0 = const()[name = string("op_391_axes_0"), val = tensor([1])]; - tensor var_391_cast_fp16 = expand_dims(axes = var_391_axes_0, x = var_322_cast_fp16_5)[name = string("op_391_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_69_to_fp16, x = input_47_cast_fp16)[name = string("x_7_cast_fp16")]; - tensor var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor([1])]; - tensor var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_322_cast_fp16_4)[name = string("op_394_cast_fp16")]; - fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p+0)]; - tensor var_396_cast_fp16 = add(x = var_394_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; - tensor var_397_cast_fp16 = mul(x = x_7_cast_fp16, y = var_396_cast_fp16)[name = string("op_397_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_322_cast_fp16_3)[name = string("op_398_cast_fp16")]; - tensor input_49_cast_fp16 = add(x = var_397_cast_fp16, y = var_398_cast_fp16)[name = string("input_49_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(4068672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5117312))))[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(5121472)))]; - tensor linear_13_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_49_cast_fp16)[name = string("linear_13_cast_fp16")]; - string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; - tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_13_cast_fp16)[name = string("input_53_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(5125632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6174272))))[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(6175360)))]; - tensor linear_14_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_53_cast_fp16)[name = string("linear_14_cast_fp16")]; - tensor var_411_cast_fp16 = mul(x = var_391_cast_fp16, y = linear_14_cast_fp16)[name = string("op_411_cast_fp16")]; - tensor input_61_cast_fp16 = add(x = input_47_cast_fp16, y = var_411_cast_fp16)[name = string("input_61_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(6176448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7749376))))[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(7755584)))]; - tensor linear_15_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_39_cast_fp16)[name = string("linear_15_cast_fp16")]; - tensor var_420_split_sizes_0 = const()[name = string("op_420_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_420_axis_0 = const()[name = string("op_420_axis_0"), val = int32(-1)]; - tensor var_420_cast_fp16_0, tensor var_420_cast_fp16_1, tensor var_420_cast_fp16_2, tensor var_420_cast_fp16_3, tensor var_420_cast_fp16_4, tensor var_420_cast_fp16_5 = split(axis = var_420_axis_0, split_sizes = var_420_split_sizes_0, x = linear_15_cast_fp16)[name = string("op_420_cast_fp16")]; - tensor var_426_axes_0 = const()[name = string("op_426_axes_0"), val = tensor([1])]; - tensor var_426_cast_fp16 = expand_dims(axes = var_426_axes_0, x = var_420_cast_fp16_2)[name = string("op_426_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_69_to_fp16, x = input_61_cast_fp16)[name = string("x_9_cast_fp16")]; - tensor var_429_axes_0 = const()[name = string("op_429_axes_0"), val = tensor([1])]; - tensor var_429_cast_fp16 = expand_dims(axes = var_429_axes_0, x = var_420_cast_fp16_1)[name = string("op_429_cast_fp16")]; - fp16 var_430_to_fp16 = const()[name = string("op_430_to_fp16"), val = fp16(0x1p+0)]; - tensor var_431_cast_fp16 = add(x = var_429_cast_fp16, y = var_430_to_fp16)[name = string("op_431_cast_fp16")]; - tensor var_432_cast_fp16 = mul(x = x_9_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_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_420_cast_fp16_0)[name = string("op_433_cast_fp16")]; - tensor x_11_cast_fp16 = add(x = var_432_cast_fp16, y = var_433_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(7761792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548288))))[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(8551424)))]; - tensor linear_16_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_16_cast_fp16")]; - tensor var_445 = const()[name = string("op_445"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_3_cast_fp16 = reshape(shape = var_445, x = linear_16_cast_fp16)[name = string("qkv_3_cast_fp16")]; - tensor var_447_split_sizes_0 = const()[name = string("op_447_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_447_axis_0 = const()[name = string("op_447_axis_0"), val = int32(2)]; - tensor var_447_cast_fp16_0, tensor var_447_cast_fp16_1, tensor var_447_cast_fp16_2 = split(axis = var_447_axis_0, split_sizes = var_447_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_447_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_447_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_447_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_447_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; - tensor var_452_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_452_cast_fp16")]; - tensor var_454_axes_0 = const()[name = string("op_454_axes_0"), val = tensor([-1])]; - bool var_454_keep_dims_0 = const()[name = string("op_454_keep_dims_0"), val = bool(true)]; - tensor var_454_cast_fp16 = reduce_mean(axes = var_454_axes_0, keep_dims = var_454_keep_dims_0, x = var_452_cast_fp16)[name = string("op_454_cast_fp16")]; - fp16 var_455_to_fp16 = const()[name = string("op_455_to_fp16"), val = fp16(0x1p-23)]; - tensor var_456_cast_fp16 = add(x = var_454_cast_fp16, y = var_455_to_fp16)[name = string("op_456_cast_fp16")]; - fp32 var_457_epsilon_0 = const()[name = string("op_457_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_457_cast_fp16 = rsqrt(epsilon = var_457_epsilon_0, x = var_456_cast_fp16)[name = string("op_457_cast_fp16")]; - tensor var_458_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_457_cast_fp16)[name = string("op_458_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(8554560)))]; - tensor var_459_cast_fp16 = mul(x = var_458_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_459_cast_fp16")]; - tensor var_462_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_462_cast_fp16")]; - tensor var_464_axes_0 = const()[name = string("op_464_axes_0"), val = tensor([-1])]; - bool var_464_keep_dims_0 = const()[name = string("op_464_keep_dims_0"), val = bool(true)]; - tensor var_464_cast_fp16 = reduce_mean(axes = var_464_axes_0, keep_dims = var_464_keep_dims_0, x = var_462_cast_fp16)[name = string("op_464_cast_fp16")]; - fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1p-23)]; - tensor var_466_cast_fp16 = add(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("op_466_cast_fp16")]; - fp32 var_467_epsilon_0 = const()[name = string("op_467_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_467_cast_fp16 = rsqrt(epsilon = var_467_epsilon_0, x = var_466_cast_fp16)[name = string("op_467_cast_fp16")]; - tensor var_468_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_467_cast_fp16)[name = string("op_468_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(8554752)))]; - tensor var_469_cast_fp16 = mul(x = var_468_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_469_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_469_cast_fp16)[name = string("transpose_151")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_459_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_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, 608, 512])]; - tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; - tensor input_63_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("input_63_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(8554944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8817152))))[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(8818240)))]; - tensor linear_17_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_63_cast_fp16)[name = string("linear_17_cast_fp16")]; - tensor var_487_cast_fp16 = mul(x = var_426_cast_fp16, y = linear_17_cast_fp16)[name = string("op_487_cast_fp16")]; - tensor input_67_cast_fp16 = add(x = input_61_cast_fp16, y = var_487_cast_fp16)[name = string("input_67_cast_fp16")]; - tensor var_489_axes_0 = const()[name = string("op_489_axes_0"), val = tensor([1])]; - tensor var_489_cast_fp16 = expand_dims(axes = var_489_axes_0, x = var_420_cast_fp16_5)[name = string("op_489_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_69_to_fp16, x = input_67_cast_fp16)[name = string("x_13_cast_fp16")]; - tensor var_492_axes_0 = const()[name = string("op_492_axes_0"), val = tensor([1])]; - tensor var_492_cast_fp16 = expand_dims(axes = var_492_axes_0, x = var_420_cast_fp16_4)[name = string("op_492_cast_fp16")]; - fp16 var_493_to_fp16 = const()[name = string("op_493_to_fp16"), val = fp16(0x1p+0)]; - tensor var_494_cast_fp16 = add(x = var_492_cast_fp16, y = var_493_to_fp16)[name = string("op_494_cast_fp16")]; - tensor var_495_cast_fp16 = mul(x = x_13_cast_fp16, y = var_494_cast_fp16)[name = string("op_495_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_420_cast_fp16_3)[name = string("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = var_495_cast_fp16, y = var_496_cast_fp16)[name = string("input_69_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(8819328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867968))))[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(9872128)))]; - tensor linear_18_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_69_cast_fp16)[name = string("linear_18_cast_fp16")]; - string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")]; - tensor input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = linear_18_cast_fp16)[name = string("input_73_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(9876288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10924928))))[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(10926016)))]; - tensor linear_19_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_73_cast_fp16)[name = string("linear_19_cast_fp16")]; - tensor var_509_cast_fp16 = mul(x = var_489_cast_fp16, y = linear_19_cast_fp16)[name = string("op_509_cast_fp16")]; - tensor input_81_cast_fp16 = add(x = input_67_cast_fp16, y = var_509_cast_fp16)[name = string("input_81_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(10927104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12500032))))[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(12506240)))]; - tensor linear_20_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_39_cast_fp16)[name = string("linear_20_cast_fp16")]; - tensor var_518_split_sizes_0 = const()[name = string("op_518_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_518_axis_0 = const()[name = string("op_518_axis_0"), val = int32(-1)]; - tensor var_518_cast_fp16_0, tensor var_518_cast_fp16_1, tensor var_518_cast_fp16_2, tensor var_518_cast_fp16_3, tensor var_518_cast_fp16_4, tensor var_518_cast_fp16_5 = split(axis = var_518_axis_0, split_sizes = var_518_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_518_cast_fp16")]; - tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; - tensor var_524_cast_fp16 = expand_dims(axes = var_524_axes_0, x = var_518_cast_fp16_2)[name = string("op_524_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_69_to_fp16, x = input_81_cast_fp16)[name = string("x_15_cast_fp16")]; - tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([1])]; - tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = var_518_cast_fp16_1)[name = string("op_527_cast_fp16")]; - fp16 var_528_to_fp16 = const()[name = string("op_528_to_fp16"), val = fp16(0x1p+0)]; - tensor var_529_cast_fp16 = add(x = var_527_cast_fp16, y = var_528_to_fp16)[name = string("op_529_cast_fp16")]; - tensor var_530_cast_fp16 = mul(x = x_15_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_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_518_cast_fp16_0)[name = string("op_531_cast_fp16")]; - tensor x_17_cast_fp16 = add(x = var_530_cast_fp16, y = var_531_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(12512448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13298944))))[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(13302080)))]; - tensor linear_21_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_21_cast_fp16")]; - tensor var_543 = const()[name = string("op_543"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_5_cast_fp16 = reshape(shape = var_543, x = linear_21_cast_fp16)[name = string("qkv_5_cast_fp16")]; - tensor var_545_split_sizes_0 = const()[name = string("op_545_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_545_axis_0 = const()[name = string("op_545_axis_0"), val = int32(2)]; - tensor var_545_cast_fp16_0, tensor var_545_cast_fp16_1, tensor var_545_cast_fp16_2 = split(axis = var_545_axis_0, split_sizes = var_545_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_545_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_545_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_545_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_545_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; - tensor var_550_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_550_cast_fp16")]; - tensor var_552_axes_0 = const()[name = string("op_552_axes_0"), val = tensor([-1])]; - bool var_552_keep_dims_0 = const()[name = string("op_552_keep_dims_0"), val = bool(true)]; - tensor var_552_cast_fp16 = reduce_mean(axes = var_552_axes_0, keep_dims = var_552_keep_dims_0, x = var_550_cast_fp16)[name = string("op_552_cast_fp16")]; - fp16 var_553_to_fp16 = const()[name = string("op_553_to_fp16"), val = fp16(0x1p-23)]; - tensor var_554_cast_fp16 = add(x = var_552_cast_fp16, y = var_553_to_fp16)[name = string("op_554_cast_fp16")]; - fp32 var_555_epsilon_0 = const()[name = string("op_555_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_555_cast_fp16 = rsqrt(epsilon = var_555_epsilon_0, x = var_554_cast_fp16)[name = string("op_555_cast_fp16")]; - tensor var_556_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_555_cast_fp16)[name = string("op_556_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(13305216)))]; - tensor var_557_cast_fp16 = mul(x = var_556_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_557_cast_fp16")]; - tensor var_560_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_560_cast_fp16")]; - tensor var_562_axes_0 = const()[name = string("op_562_axes_0"), val = tensor([-1])]; - bool var_562_keep_dims_0 = const()[name = string("op_562_keep_dims_0"), val = bool(true)]; - tensor var_562_cast_fp16 = reduce_mean(axes = var_562_axes_0, keep_dims = var_562_keep_dims_0, x = var_560_cast_fp16)[name = string("op_562_cast_fp16")]; - fp16 var_563_to_fp16 = const()[name = string("op_563_to_fp16"), val = fp16(0x1p-23)]; - tensor var_564_cast_fp16 = add(x = var_562_cast_fp16, y = var_563_to_fp16)[name = string("op_564_cast_fp16")]; - fp32 var_565_epsilon_0 = const()[name = string("op_565_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_565_cast_fp16 = rsqrt(epsilon = var_565_epsilon_0, x = var_564_cast_fp16)[name = string("op_565_cast_fp16")]; - tensor var_566_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_565_cast_fp16)[name = string("op_566_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(13305408)))]; - tensor var_567_cast_fp16 = mul(x = var_566_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_567_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_567_cast_fp16)[name = string("transpose_147")]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_557_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_578_perm_0 = const()[name = string("op_578_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_579 = const()[name = string("op_579"), val = tensor([1, 608, 512])]; - tensor var_578_cast_fp16 = transpose(perm = var_578_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; - tensor input_83_cast_fp16 = reshape(shape = var_579, x = var_578_cast_fp16)[name = string("input_83_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(13305600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13567808))))[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(13568896)))]; - tensor linear_22_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_83_cast_fp16)[name = string("linear_22_cast_fp16")]; - tensor var_585_cast_fp16 = mul(x = var_524_cast_fp16, y = linear_22_cast_fp16)[name = string("op_585_cast_fp16")]; - tensor input_87_cast_fp16 = add(x = input_81_cast_fp16, y = var_585_cast_fp16)[name = string("input_87_cast_fp16")]; - tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([1])]; - tensor var_587_cast_fp16 = expand_dims(axes = var_587_axes_0, x = var_518_cast_fp16_5)[name = string("op_587_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_69_to_fp16, x = input_87_cast_fp16)[name = string("x_19_cast_fp16")]; - tensor var_590_axes_0 = const()[name = string("op_590_axes_0"), val = tensor([1])]; - tensor var_590_cast_fp16 = expand_dims(axes = var_590_axes_0, x = var_518_cast_fp16_4)[name = string("op_590_cast_fp16")]; - fp16 var_591_to_fp16 = const()[name = string("op_591_to_fp16"), val = fp16(0x1p+0)]; - tensor var_592_cast_fp16 = add(x = var_590_cast_fp16, y = var_591_to_fp16)[name = string("op_592_cast_fp16")]; - tensor var_593_cast_fp16 = mul(x = x_19_cast_fp16, y = var_592_cast_fp16)[name = string("op_593_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_518_cast_fp16_3)[name = string("op_594_cast_fp16")]; - tensor input_89_cast_fp16 = add(x = var_593_cast_fp16, y = var_594_cast_fp16)[name = string("input_89_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(13569984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14618624))))[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(14622784)))]; - tensor linear_23_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_89_cast_fp16)[name = string("linear_23_cast_fp16")]; - string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")]; - tensor input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = linear_23_cast_fp16)[name = string("input_93_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(14626944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15675584))))[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(15676672)))]; - tensor linear_24_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_93_cast_fp16)[name = string("linear_24_cast_fp16")]; - tensor var_607_cast_fp16 = mul(x = var_587_cast_fp16, y = linear_24_cast_fp16)[name = string("op_607_cast_fp16")]; - tensor input_101_cast_fp16 = add(x = input_87_cast_fp16, y = var_607_cast_fp16)[name = string("input_101_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(15677760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17250688))))[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(17256896)))]; - tensor linear_25_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_39_cast_fp16)[name = string("linear_25_cast_fp16")]; - tensor var_616_split_sizes_0 = const()[name = string("op_616_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_616_axis_0 = const()[name = string("op_616_axis_0"), val = int32(-1)]; - tensor var_616_cast_fp16_0, tensor var_616_cast_fp16_1, tensor var_616_cast_fp16_2, tensor var_616_cast_fp16_3, tensor var_616_cast_fp16_4, tensor var_616_cast_fp16_5 = split(axis = var_616_axis_0, split_sizes = var_616_split_sizes_0, x = linear_25_cast_fp16)[name = string("op_616_cast_fp16")]; - tensor var_622_axes_0 = const()[name = string("op_622_axes_0"), val = tensor([1])]; - tensor var_622_cast_fp16 = expand_dims(axes = var_622_axes_0, x = var_616_cast_fp16_2)[name = string("op_622_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_69_to_fp16, x = input_101_cast_fp16)[name = string("x_21_cast_fp16")]; - tensor var_625_axes_0 = const()[name = string("op_625_axes_0"), val = tensor([1])]; - tensor var_625_cast_fp16 = expand_dims(axes = var_625_axes_0, x = var_616_cast_fp16_1)[name = string("op_625_cast_fp16")]; - fp16 var_626_to_fp16 = const()[name = string("op_626_to_fp16"), val = fp16(0x1p+0)]; - tensor var_627_cast_fp16 = add(x = var_625_cast_fp16, y = var_626_to_fp16)[name = string("op_627_cast_fp16")]; - tensor var_628_cast_fp16 = mul(x = x_21_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_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_616_cast_fp16_0)[name = string("op_629_cast_fp16")]; - tensor x_23_cast_fp16 = add(x = var_628_cast_fp16, y = var_629_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(17263104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18049600))))[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(18052736)))]; - tensor linear_26_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_26_cast_fp16")]; - tensor var_641 = const()[name = string("op_641"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_7_cast_fp16 = reshape(shape = var_641, x = linear_26_cast_fp16)[name = string("qkv_7_cast_fp16")]; - tensor var_643_split_sizes_0 = const()[name = string("op_643_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_643_axis_0 = const()[name = string("op_643_axis_0"), val = int32(2)]; - tensor var_643_cast_fp16_0, tensor var_643_cast_fp16_1, tensor var_643_cast_fp16_2 = split(axis = var_643_axis_0, split_sizes = var_643_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_643_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_643_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_643_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_643_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; - tensor var_648_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_648_cast_fp16")]; - tensor var_650_axes_0 = const()[name = string("op_650_axes_0"), val = tensor([-1])]; - bool var_650_keep_dims_0 = const()[name = string("op_650_keep_dims_0"), val = bool(true)]; - tensor var_650_cast_fp16 = reduce_mean(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = string("op_650_cast_fp16")]; - fp16 var_651_to_fp16 = const()[name = string("op_651_to_fp16"), val = fp16(0x1p-23)]; - tensor var_652_cast_fp16 = add(x = var_650_cast_fp16, y = var_651_to_fp16)[name = string("op_652_cast_fp16")]; - fp32 var_653_epsilon_0 = const()[name = string("op_653_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_653_cast_fp16 = rsqrt(epsilon = var_653_epsilon_0, x = var_652_cast_fp16)[name = string("op_653_cast_fp16")]; - tensor var_654_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_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(18055872)))]; - tensor var_655_cast_fp16 = mul(x = var_654_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_655_cast_fp16")]; - tensor var_658_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_658_cast_fp16")]; - tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; - bool var_660_keep_dims_0 = const()[name = string("op_660_keep_dims_0"), val = bool(true)]; - tensor var_660_cast_fp16 = reduce_mean(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = string("op_660_cast_fp16")]; - fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-23)]; - tensor var_662_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("op_662_cast_fp16")]; - fp32 var_663_epsilon_0 = const()[name = string("op_663_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_663_cast_fp16 = rsqrt(epsilon = var_663_epsilon_0, x = var_662_cast_fp16)[name = string("op_663_cast_fp16")]; - tensor var_664_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_663_cast_fp16)[name = string("op_664_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(18056064)))]; - tensor var_665_cast_fp16 = mul(x = var_664_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_665_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_665_cast_fp16)[name = string("transpose_143")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_655_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_676_perm_0 = const()[name = string("op_676_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_677 = const()[name = string("op_677"), val = tensor([1, 608, 512])]; - tensor var_676_cast_fp16 = transpose(perm = var_676_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; - tensor input_103_cast_fp16 = reshape(shape = var_677, x = var_676_cast_fp16)[name = string("input_103_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(18056256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18318464))))[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(18319552)))]; - tensor linear_27_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_103_cast_fp16)[name = string("linear_27_cast_fp16")]; - tensor var_683_cast_fp16 = mul(x = var_622_cast_fp16, y = linear_27_cast_fp16)[name = string("op_683_cast_fp16")]; - tensor input_107_cast_fp16 = add(x = input_101_cast_fp16, y = var_683_cast_fp16)[name = string("input_107_cast_fp16")]; - tensor var_685_axes_0 = const()[name = string("op_685_axes_0"), val = tensor([1])]; - tensor var_685_cast_fp16 = expand_dims(axes = var_685_axes_0, x = var_616_cast_fp16_5)[name = string("op_685_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_69_to_fp16, x = input_107_cast_fp16)[name = string("x_25_cast_fp16")]; - tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([1])]; - tensor var_688_cast_fp16 = expand_dims(axes = var_688_axes_0, x = var_616_cast_fp16_4)[name = string("op_688_cast_fp16")]; - fp16 var_689_to_fp16 = const()[name = string("op_689_to_fp16"), val = fp16(0x1p+0)]; - tensor var_690_cast_fp16 = add(x = var_688_cast_fp16, y = var_689_to_fp16)[name = string("op_690_cast_fp16")]; - tensor var_691_cast_fp16 = mul(x = x_25_cast_fp16, y = var_690_cast_fp16)[name = string("op_691_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_616_cast_fp16_3)[name = string("op_692_cast_fp16")]; - tensor input_109_cast_fp16 = add(x = var_691_cast_fp16, y = var_692_cast_fp16)[name = string("input_109_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(18320640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19369280))))[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(19373440)))]; - tensor linear_28_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_109_cast_fp16)[name = string("linear_28_cast_fp16")]; - string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; - tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_28_cast_fp16)[name = string("input_113_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(19377600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20426240))))[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(20427328)))]; - tensor linear_29_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_113_cast_fp16)[name = string("linear_29_cast_fp16")]; - tensor var_705_cast_fp16 = mul(x = var_685_cast_fp16, y = linear_29_cast_fp16)[name = string("op_705_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_107_cast_fp16, y = var_705_cast_fp16)[name = string("input_121_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(20428416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001344))))[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(22007552)))]; - tensor linear_30_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_39_cast_fp16)[name = string("linear_30_cast_fp16")]; - tensor var_714_split_sizes_0 = const()[name = string("op_714_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_714_axis_0 = const()[name = string("op_714_axis_0"), val = int32(-1)]; - tensor var_714_cast_fp16_0, tensor var_714_cast_fp16_1, tensor var_714_cast_fp16_2, tensor var_714_cast_fp16_3, tensor var_714_cast_fp16_4, tensor var_714_cast_fp16_5 = split(axis = var_714_axis_0, split_sizes = var_714_split_sizes_0, x = linear_30_cast_fp16)[name = string("op_714_cast_fp16")]; - tensor var_720_axes_0 = const()[name = string("op_720_axes_0"), val = tensor([1])]; - tensor var_720_cast_fp16 = expand_dims(axes = var_720_axes_0, x = var_714_cast_fp16_2)[name = string("op_720_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_69_to_fp16, x = input_121_cast_fp16)[name = string("x_27_cast_fp16")]; - tensor var_723_axes_0 = const()[name = string("op_723_axes_0"), val = tensor([1])]; - tensor var_723_cast_fp16 = expand_dims(axes = var_723_axes_0, x = var_714_cast_fp16_1)[name = string("op_723_cast_fp16")]; - fp16 var_724_to_fp16 = const()[name = string("op_724_to_fp16"), val = fp16(0x1p+0)]; - tensor var_725_cast_fp16 = add(x = var_723_cast_fp16, y = var_724_to_fp16)[name = string("op_725_cast_fp16")]; - tensor var_726_cast_fp16 = mul(x = x_27_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_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_714_cast_fp16_0)[name = string("op_727_cast_fp16")]; - tensor x_29_cast_fp16 = add(x = var_726_cast_fp16, y = var_727_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(22013760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22800256))))[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(22803392)))]; - tensor linear_31_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_31_cast_fp16")]; - tensor var_739 = const()[name = string("op_739"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_9_cast_fp16 = reshape(shape = var_739, x = linear_31_cast_fp16)[name = string("qkv_9_cast_fp16")]; - tensor var_741_split_sizes_0 = const()[name = string("op_741_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_741_axis_0 = const()[name = string("op_741_axis_0"), val = int32(2)]; - tensor var_741_cast_fp16_0, tensor var_741_cast_fp16_1, tensor var_741_cast_fp16_2 = split(axis = var_741_axis_0, split_sizes = var_741_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_741_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_741_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_741_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_741_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; - tensor var_746_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_746_cast_fp16")]; - tensor var_748_axes_0 = const()[name = string("op_748_axes_0"), val = tensor([-1])]; - bool var_748_keep_dims_0 = const()[name = string("op_748_keep_dims_0"), val = bool(true)]; - tensor var_748_cast_fp16 = reduce_mean(axes = var_748_axes_0, keep_dims = var_748_keep_dims_0, x = var_746_cast_fp16)[name = string("op_748_cast_fp16")]; - fp16 var_749_to_fp16 = const()[name = string("op_749_to_fp16"), val = fp16(0x1p-23)]; - tensor var_750_cast_fp16 = add(x = var_748_cast_fp16, y = var_749_to_fp16)[name = string("op_750_cast_fp16")]; - fp32 var_751_epsilon_0 = const()[name = string("op_751_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_751_cast_fp16 = rsqrt(epsilon = var_751_epsilon_0, x = var_750_cast_fp16)[name = string("op_751_cast_fp16")]; - tensor var_752_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_751_cast_fp16)[name = string("op_752_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(22806528)))]; - tensor var_753_cast_fp16 = mul(x = var_752_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_753_cast_fp16")]; - tensor var_756_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_756_cast_fp16")]; - tensor var_758_axes_0 = const()[name = string("op_758_axes_0"), val = tensor([-1])]; - bool var_758_keep_dims_0 = const()[name = string("op_758_keep_dims_0"), val = bool(true)]; - tensor var_758_cast_fp16 = reduce_mean(axes = var_758_axes_0, keep_dims = var_758_keep_dims_0, x = var_756_cast_fp16)[name = string("op_758_cast_fp16")]; - fp16 var_759_to_fp16 = const()[name = string("op_759_to_fp16"), val = fp16(0x1p-23)]; - tensor var_760_cast_fp16 = add(x = var_758_cast_fp16, y = var_759_to_fp16)[name = string("op_760_cast_fp16")]; - fp32 var_761_epsilon_0 = const()[name = string("op_761_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_761_cast_fp16 = rsqrt(epsilon = var_761_epsilon_0, x = var_760_cast_fp16)[name = string("op_761_cast_fp16")]; - tensor var_762_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_761_cast_fp16)[name = string("op_762_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(22806720)))]; - tensor var_763_cast_fp16 = mul(x = var_762_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_763_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_763_cast_fp16)[name = string("transpose_139")]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_753_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_774_perm_0 = const()[name = string("op_774_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_775 = const()[name = string("op_775"), val = tensor([1, 608, 512])]; - tensor var_774_cast_fp16 = transpose(perm = var_774_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; - tensor input_123_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("input_123_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(22806912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23069120))))[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(23070208)))]; - tensor linear_32_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_123_cast_fp16)[name = string("linear_32_cast_fp16")]; - tensor var_781_cast_fp16 = mul(x = var_720_cast_fp16, y = linear_32_cast_fp16)[name = string("op_781_cast_fp16")]; - tensor input_127_cast_fp16 = add(x = input_121_cast_fp16, y = var_781_cast_fp16)[name = string("input_127_cast_fp16")]; - tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([1])]; - tensor var_783_cast_fp16 = expand_dims(axes = var_783_axes_0, x = var_714_cast_fp16_5)[name = string("op_783_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_69_to_fp16, x = input_127_cast_fp16)[name = string("x_31_cast_fp16")]; - tensor var_786_axes_0 = const()[name = string("op_786_axes_0"), val = tensor([1])]; - tensor var_786_cast_fp16 = expand_dims(axes = var_786_axes_0, x = var_714_cast_fp16_4)[name = string("op_786_cast_fp16")]; - fp16 var_787_to_fp16 = const()[name = string("op_787_to_fp16"), val = fp16(0x1p+0)]; - tensor var_788_cast_fp16 = add(x = var_786_cast_fp16, y = var_787_to_fp16)[name = string("op_788_cast_fp16")]; - tensor var_789_cast_fp16 = mul(x = x_31_cast_fp16, y = var_788_cast_fp16)[name = string("op_789_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_714_cast_fp16_3)[name = string("op_790_cast_fp16")]; - tensor input_129_cast_fp16 = add(x = var_789_cast_fp16, y = var_790_cast_fp16)[name = string("input_129_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(23071296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119936))))[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(24124096)))]; - tensor linear_33_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_129_cast_fp16)[name = string("linear_33_cast_fp16")]; - string input_133_mode_0 = const()[name = string("input_133_mode_0"), val = string("EXACT")]; - tensor input_133_cast_fp16 = gelu(mode = input_133_mode_0, x = linear_33_cast_fp16)[name = string("input_133_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(24128256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176896))))[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(25177984)))]; - tensor linear_34_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_133_cast_fp16)[name = string("linear_34_cast_fp16")]; - tensor var_803_cast_fp16 = mul(x = var_783_cast_fp16, y = linear_34_cast_fp16)[name = string("op_803_cast_fp16")]; - tensor input_141_cast_fp16 = add(x = input_127_cast_fp16, y = var_803_cast_fp16)[name = string("input_141_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(25179072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26752000))))[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(26758208)))]; - tensor linear_35_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_39_cast_fp16)[name = string("linear_35_cast_fp16")]; - tensor var_812_split_sizes_0 = const()[name = string("op_812_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_812_axis_0 = const()[name = string("op_812_axis_0"), val = int32(-1)]; - tensor var_812_cast_fp16_0, tensor var_812_cast_fp16_1, tensor var_812_cast_fp16_2, tensor var_812_cast_fp16_3, tensor var_812_cast_fp16_4, tensor var_812_cast_fp16_5 = split(axis = var_812_axis_0, split_sizes = var_812_split_sizes_0, x = linear_35_cast_fp16)[name = string("op_812_cast_fp16")]; - tensor var_818_axes_0 = const()[name = string("op_818_axes_0"), val = tensor([1])]; - tensor var_818_cast_fp16 = expand_dims(axes = var_818_axes_0, x = var_812_cast_fp16_2)[name = string("op_818_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_69_to_fp16, x = input_141_cast_fp16)[name = string("x_33_cast_fp16")]; - tensor var_821_axes_0 = const()[name = string("op_821_axes_0"), val = tensor([1])]; - tensor var_821_cast_fp16 = expand_dims(axes = var_821_axes_0, x = var_812_cast_fp16_1)[name = string("op_821_cast_fp16")]; - fp16 var_822_to_fp16 = const()[name = string("op_822_to_fp16"), val = fp16(0x1p+0)]; - tensor var_823_cast_fp16 = add(x = var_821_cast_fp16, y = var_822_to_fp16)[name = string("op_823_cast_fp16")]; - tensor var_824_cast_fp16 = mul(x = x_33_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_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_812_cast_fp16_0)[name = string("op_825_cast_fp16")]; - tensor x_35_cast_fp16 = add(x = var_824_cast_fp16, y = var_825_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(26764416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27550912))))[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(27554048)))]; - tensor linear_36_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_36_cast_fp16")]; - tensor var_837 = const()[name = string("op_837"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_11_cast_fp16 = reshape(shape = var_837, x = linear_36_cast_fp16)[name = string("qkv_11_cast_fp16")]; - tensor var_839_split_sizes_0 = const()[name = string("op_839_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_839_axis_0 = const()[name = string("op_839_axis_0"), val = int32(2)]; - tensor var_839_cast_fp16_0, tensor var_839_cast_fp16_1, tensor var_839_cast_fp16_2 = split(axis = var_839_axis_0, split_sizes = var_839_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_839_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_839_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_839_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_839_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; - tensor var_844_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_844_cast_fp16")]; - tensor var_846_axes_0 = const()[name = string("op_846_axes_0"), val = tensor([-1])]; - bool var_846_keep_dims_0 = const()[name = string("op_846_keep_dims_0"), val = bool(true)]; - tensor var_846_cast_fp16 = reduce_mean(axes = var_846_axes_0, keep_dims = var_846_keep_dims_0, x = var_844_cast_fp16)[name = string("op_846_cast_fp16")]; - fp16 var_847_to_fp16 = const()[name = string("op_847_to_fp16"), val = fp16(0x1p-23)]; - tensor var_848_cast_fp16 = add(x = var_846_cast_fp16, y = var_847_to_fp16)[name = string("op_848_cast_fp16")]; - fp32 var_849_epsilon_0 = const()[name = string("op_849_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_849_cast_fp16 = rsqrt(epsilon = var_849_epsilon_0, x = var_848_cast_fp16)[name = string("op_849_cast_fp16")]; - tensor var_850_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_849_cast_fp16)[name = string("op_850_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(27557184)))]; - tensor var_851_cast_fp16 = mul(x = var_850_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_851_cast_fp16")]; - tensor var_854_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_854_cast_fp16")]; - tensor var_856_axes_0 = const()[name = string("op_856_axes_0"), val = tensor([-1])]; - bool var_856_keep_dims_0 = const()[name = string("op_856_keep_dims_0"), val = bool(true)]; - tensor var_856_cast_fp16 = reduce_mean(axes = var_856_axes_0, keep_dims = var_856_keep_dims_0, x = var_854_cast_fp16)[name = string("op_856_cast_fp16")]; - fp16 var_857_to_fp16 = const()[name = string("op_857_to_fp16"), val = fp16(0x1p-23)]; - tensor var_858_cast_fp16 = add(x = var_856_cast_fp16, y = var_857_to_fp16)[name = string("op_858_cast_fp16")]; - fp32 var_859_epsilon_0 = const()[name = string("op_859_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_859_cast_fp16 = rsqrt(epsilon = var_859_epsilon_0, x = var_858_cast_fp16)[name = string("op_859_cast_fp16")]; - tensor var_860_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_859_cast_fp16)[name = string("op_860_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(27557376)))]; - tensor var_861_cast_fp16 = mul(x = var_860_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_861_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_861_cast_fp16)[name = string("transpose_135")]; - tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_851_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_872_perm_0 = const()[name = string("op_872_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_873 = const()[name = string("op_873"), val = tensor([1, 608, 512])]; - tensor var_872_cast_fp16 = transpose(perm = var_872_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; - tensor input_143_cast_fp16 = reshape(shape = var_873, x = var_872_cast_fp16)[name = string("input_143_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(27557568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27819776))))[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(27820864)))]; - tensor linear_37_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_143_cast_fp16)[name = string("linear_37_cast_fp16")]; - tensor var_879_cast_fp16 = mul(x = var_818_cast_fp16, y = linear_37_cast_fp16)[name = string("op_879_cast_fp16")]; - tensor input_147_cast_fp16 = add(x = input_141_cast_fp16, y = var_879_cast_fp16)[name = string("input_147_cast_fp16")]; - tensor var_881_axes_0 = const()[name = string("op_881_axes_0"), val = tensor([1])]; - tensor var_881_cast_fp16 = expand_dims(axes = var_881_axes_0, x = var_812_cast_fp16_5)[name = string("op_881_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_69_to_fp16, x = input_147_cast_fp16)[name = string("x_37_cast_fp16")]; - tensor var_884_axes_0 = const()[name = string("op_884_axes_0"), val = tensor([1])]; - tensor var_884_cast_fp16 = expand_dims(axes = var_884_axes_0, x = var_812_cast_fp16_4)[name = string("op_884_cast_fp16")]; - fp16 var_885_to_fp16 = const()[name = string("op_885_to_fp16"), val = fp16(0x1p+0)]; - tensor var_886_cast_fp16 = add(x = var_884_cast_fp16, y = var_885_to_fp16)[name = string("op_886_cast_fp16")]; - tensor var_887_cast_fp16 = mul(x = x_37_cast_fp16, y = var_886_cast_fp16)[name = string("op_887_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_812_cast_fp16_3)[name = string("op_888_cast_fp16")]; - tensor input_149_cast_fp16 = add(x = var_887_cast_fp16, y = var_888_cast_fp16)[name = string("input_149_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(27821952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28870592))))[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(28874752)))]; - tensor linear_38_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_149_cast_fp16)[name = string("linear_38_cast_fp16")]; - string input_153_mode_0 = const()[name = string("input_153_mode_0"), val = string("EXACT")]; - tensor input_153_cast_fp16 = gelu(mode = input_153_mode_0, x = linear_38_cast_fp16)[name = string("input_153_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(28878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29927552))))[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(29928640)))]; - tensor linear_39_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_153_cast_fp16)[name = string("linear_39_cast_fp16")]; - tensor var_901_cast_fp16 = mul(x = var_881_cast_fp16, y = linear_39_cast_fp16)[name = string("op_901_cast_fp16")]; - tensor input_161_cast_fp16 = add(x = input_147_cast_fp16, y = var_901_cast_fp16)[name = string("input_161_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(29929728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31502656))))[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(31508864)))]; - tensor linear_40_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_39_cast_fp16)[name = string("linear_40_cast_fp16")]; - tensor var_910_split_sizes_0 = const()[name = string("op_910_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_910_axis_0 = const()[name = string("op_910_axis_0"), val = int32(-1)]; - tensor var_910_cast_fp16_0, tensor var_910_cast_fp16_1, tensor var_910_cast_fp16_2, tensor var_910_cast_fp16_3, tensor var_910_cast_fp16_4, tensor var_910_cast_fp16_5 = split(axis = var_910_axis_0, split_sizes = var_910_split_sizes_0, x = linear_40_cast_fp16)[name = string("op_910_cast_fp16")]; - tensor var_916_axes_0 = const()[name = string("op_916_axes_0"), val = tensor([1])]; - tensor var_916_cast_fp16 = expand_dims(axes = var_916_axes_0, x = var_910_cast_fp16_2)[name = string("op_916_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_69_to_fp16, x = input_161_cast_fp16)[name = string("x_39_cast_fp16")]; - tensor var_919_axes_0 = const()[name = string("op_919_axes_0"), val = tensor([1])]; - tensor var_919_cast_fp16 = expand_dims(axes = var_919_axes_0, x = var_910_cast_fp16_1)[name = string("op_919_cast_fp16")]; - fp16 var_920_to_fp16 = const()[name = string("op_920_to_fp16"), val = fp16(0x1p+0)]; - tensor var_921_cast_fp16 = add(x = var_919_cast_fp16, y = var_920_to_fp16)[name = string("op_921_cast_fp16")]; - tensor var_922_cast_fp16 = mul(x = x_39_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_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_910_cast_fp16_0)[name = string("op_923_cast_fp16")]; - tensor x_41_cast_fp16 = add(x = var_922_cast_fp16, y = var_923_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(31515072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32301568))))[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(32304704)))]; - tensor linear_41_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_41_cast_fp16")]; - tensor var_935 = const()[name = string("op_935"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_13_cast_fp16 = reshape(shape = var_935, x = linear_41_cast_fp16)[name = string("qkv_13_cast_fp16")]; - tensor var_937_split_sizes_0 = const()[name = string("op_937_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_937_axis_0 = const()[name = string("op_937_axis_0"), val = int32(2)]; - tensor var_937_cast_fp16_0, tensor var_937_cast_fp16_1, tensor var_937_cast_fp16_2 = split(axis = var_937_axis_0, split_sizes = var_937_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_937_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_937_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_937_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_937_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; - tensor var_942_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_942_cast_fp16")]; - tensor var_944_axes_0 = const()[name = string("op_944_axes_0"), val = tensor([-1])]; - bool var_944_keep_dims_0 = const()[name = string("op_944_keep_dims_0"), val = bool(true)]; - tensor var_944_cast_fp16 = reduce_mean(axes = var_944_axes_0, keep_dims = var_944_keep_dims_0, x = var_942_cast_fp16)[name = string("op_944_cast_fp16")]; - fp16 var_945_to_fp16 = const()[name = string("op_945_to_fp16"), val = fp16(0x1p-23)]; - tensor var_946_cast_fp16 = add(x = var_944_cast_fp16, y = var_945_to_fp16)[name = string("op_946_cast_fp16")]; - fp32 var_947_epsilon_0 = const()[name = string("op_947_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_947_cast_fp16 = rsqrt(epsilon = var_947_epsilon_0, x = var_946_cast_fp16)[name = string("op_947_cast_fp16")]; - tensor var_948_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_947_cast_fp16)[name = string("op_948_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(32307840)))]; - tensor var_949_cast_fp16 = mul(x = var_948_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_949_cast_fp16")]; - tensor var_952_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_952_cast_fp16")]; - tensor var_954_axes_0 = const()[name = string("op_954_axes_0"), val = tensor([-1])]; - bool var_954_keep_dims_0 = const()[name = string("op_954_keep_dims_0"), val = bool(true)]; - tensor var_954_cast_fp16 = reduce_mean(axes = var_954_axes_0, keep_dims = var_954_keep_dims_0, x = var_952_cast_fp16)[name = string("op_954_cast_fp16")]; - fp16 var_955_to_fp16 = const()[name = string("op_955_to_fp16"), val = fp16(0x1p-23)]; - tensor var_956_cast_fp16 = add(x = var_954_cast_fp16, y = var_955_to_fp16)[name = string("op_956_cast_fp16")]; - fp32 var_957_epsilon_0 = const()[name = string("op_957_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_957_cast_fp16 = rsqrt(epsilon = var_957_epsilon_0, x = var_956_cast_fp16)[name = string("op_957_cast_fp16")]; - tensor var_958_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_957_cast_fp16)[name = string("op_958_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(32308032)))]; - tensor var_959_cast_fp16 = mul(x = var_958_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_959_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_959_cast_fp16)[name = string("transpose_131")]; - tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_949_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_970_perm_0 = const()[name = string("op_970_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_971 = const()[name = string("op_971"), val = tensor([1, 608, 512])]; - tensor var_970_cast_fp16 = transpose(perm = var_970_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; - tensor input_163_cast_fp16 = reshape(shape = var_971, x = var_970_cast_fp16)[name = string("input_163_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(32308224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32570432))))[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(32571520)))]; - tensor linear_42_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_163_cast_fp16)[name = string("linear_42_cast_fp16")]; - tensor var_977_cast_fp16 = mul(x = var_916_cast_fp16, y = linear_42_cast_fp16)[name = string("op_977_cast_fp16")]; - tensor input_167_cast_fp16 = add(x = input_161_cast_fp16, y = var_977_cast_fp16)[name = string("input_167_cast_fp16")]; - tensor var_979_axes_0 = const()[name = string("op_979_axes_0"), val = tensor([1])]; - tensor var_979_cast_fp16 = expand_dims(axes = var_979_axes_0, x = var_910_cast_fp16_5)[name = string("op_979_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_69_to_fp16, x = input_167_cast_fp16)[name = string("x_43_cast_fp16")]; - tensor var_982_axes_0 = const()[name = string("op_982_axes_0"), val = tensor([1])]; - tensor var_982_cast_fp16 = expand_dims(axes = var_982_axes_0, x = var_910_cast_fp16_4)[name = string("op_982_cast_fp16")]; - fp16 var_983_to_fp16 = const()[name = string("op_983_to_fp16"), val = fp16(0x1p+0)]; - tensor var_984_cast_fp16 = add(x = var_982_cast_fp16, y = var_983_to_fp16)[name = string("op_984_cast_fp16")]; - tensor var_985_cast_fp16 = mul(x = x_43_cast_fp16, y = var_984_cast_fp16)[name = string("op_985_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_910_cast_fp16_3)[name = string("op_986_cast_fp16")]; - tensor input_169_cast_fp16 = add(x = var_985_cast_fp16, y = var_986_cast_fp16)[name = string("input_169_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(32572608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33621248))))[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(33625408)))]; - tensor linear_43_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_169_cast_fp16)[name = string("linear_43_cast_fp16")]; - string input_173_mode_0 = const()[name = string("input_173_mode_0"), val = string("EXACT")]; - tensor input_173_cast_fp16 = gelu(mode = input_173_mode_0, x = linear_43_cast_fp16)[name = string("input_173_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(33629568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34678208))))[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(34679296)))]; - tensor linear_44_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_173_cast_fp16)[name = string("linear_44_cast_fp16")]; - tensor var_999_cast_fp16 = mul(x = var_979_cast_fp16, y = linear_44_cast_fp16)[name = string("op_999_cast_fp16")]; - tensor input_181_cast_fp16 = add(x = input_167_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_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(34680384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36253312))))[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(36259520)))]; - tensor linear_45_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_39_cast_fp16)[name = string("linear_45_cast_fp16")]; - tensor var_1008_split_sizes_0 = const()[name = string("op_1008_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1008_axis_0 = const()[name = string("op_1008_axis_0"), val = int32(-1)]; - tensor var_1008_cast_fp16_0, tensor var_1008_cast_fp16_1, tensor var_1008_cast_fp16_2, tensor var_1008_cast_fp16_3, tensor var_1008_cast_fp16_4, tensor var_1008_cast_fp16_5 = split(axis = var_1008_axis_0, split_sizes = var_1008_split_sizes_0, x = linear_45_cast_fp16)[name = string("op_1008_cast_fp16")]; - tensor var_1014_axes_0 = const()[name = string("op_1014_axes_0"), val = tensor([1])]; - tensor var_1014_cast_fp16 = expand_dims(axes = var_1014_axes_0, x = var_1008_cast_fp16_2)[name = string("op_1014_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_69_to_fp16, x = input_181_cast_fp16)[name = string("x_45_cast_fp16")]; - tensor var_1017_axes_0 = const()[name = string("op_1017_axes_0"), val = tensor([1])]; - tensor var_1017_cast_fp16 = expand_dims(axes = var_1017_axes_0, x = var_1008_cast_fp16_1)[name = string("op_1017_cast_fp16")]; - fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1019_cast_fp16 = add(x = var_1017_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; - tensor var_1020_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_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_1008_cast_fp16_0)[name = string("op_1021_cast_fp16")]; - tensor x_47_cast_fp16 = add(x = var_1020_cast_fp16, y = var_1021_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(36265728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37052224))))[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(37055360)))]; - tensor linear_46_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_46_cast_fp16")]; - tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_15_cast_fp16 = reshape(shape = var_1033, x = linear_46_cast_fp16)[name = string("qkv_15_cast_fp16")]; - tensor var_1035_split_sizes_0 = const()[name = string("op_1035_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1035_axis_0 = const()[name = string("op_1035_axis_0"), val = int32(2)]; - tensor var_1035_cast_fp16_0, tensor var_1035_cast_fp16_1, tensor var_1035_cast_fp16_2 = split(axis = var_1035_axis_0, split_sizes = var_1035_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1035_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_1035_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_1035_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_1035_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; - tensor var_1040_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1040_cast_fp16")]; - tensor var_1042_axes_0 = const()[name = string("op_1042_axes_0"), val = tensor([-1])]; - bool var_1042_keep_dims_0 = const()[name = string("op_1042_keep_dims_0"), val = bool(true)]; - tensor var_1042_cast_fp16 = reduce_mean(axes = var_1042_axes_0, keep_dims = var_1042_keep_dims_0, x = var_1040_cast_fp16)[name = string("op_1042_cast_fp16")]; - fp16 var_1043_to_fp16 = const()[name = string("op_1043_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1044_cast_fp16 = add(x = var_1042_cast_fp16, y = var_1043_to_fp16)[name = string("op_1044_cast_fp16")]; - fp32 var_1045_epsilon_0 = const()[name = string("op_1045_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1045_cast_fp16 = rsqrt(epsilon = var_1045_epsilon_0, x = var_1044_cast_fp16)[name = string("op_1045_cast_fp16")]; - tensor var_1046_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1045_cast_fp16)[name = string("op_1046_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(37058496)))]; - tensor var_1047_cast_fp16 = mul(x = var_1046_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1047_cast_fp16")]; - tensor var_1050_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1050_cast_fp16")]; - tensor var_1052_axes_0 = const()[name = string("op_1052_axes_0"), val = tensor([-1])]; - bool var_1052_keep_dims_0 = const()[name = string("op_1052_keep_dims_0"), val = bool(true)]; - tensor var_1052_cast_fp16 = reduce_mean(axes = var_1052_axes_0, keep_dims = var_1052_keep_dims_0, x = var_1050_cast_fp16)[name = string("op_1052_cast_fp16")]; - fp16 var_1053_to_fp16 = const()[name = string("op_1053_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1054_cast_fp16 = add(x = var_1052_cast_fp16, y = var_1053_to_fp16)[name = string("op_1054_cast_fp16")]; - fp32 var_1055_epsilon_0 = const()[name = string("op_1055_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1055_cast_fp16 = rsqrt(epsilon = var_1055_epsilon_0, x = var_1054_cast_fp16)[name = string("op_1055_cast_fp16")]; - tensor var_1056_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1055_cast_fp16)[name = string("op_1056_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(37058688)))]; - tensor var_1057_cast_fp16 = mul(x = var_1056_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1057_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_1057_cast_fp16)[name = string("transpose_127")]; - tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1047_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_1068_perm_0 = const()[name = string("op_1068_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1069 = const()[name = string("op_1069"), val = tensor([1, 608, 512])]; - tensor var_1068_cast_fp16 = transpose(perm = var_1068_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; - tensor input_183_cast_fp16 = reshape(shape = var_1069, x = var_1068_cast_fp16)[name = string("input_183_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(37058880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37321088))))[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(37322176)))]; - tensor linear_47_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_183_cast_fp16)[name = string("linear_47_cast_fp16")]; - tensor var_1075_cast_fp16 = mul(x = var_1014_cast_fp16, y = linear_47_cast_fp16)[name = string("op_1075_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_181_cast_fp16, y = var_1075_cast_fp16)[name = string("input_187_cast_fp16")]; - tensor var_1077_axes_0 = const()[name = string("op_1077_axes_0"), val = tensor([1])]; - tensor var_1077_cast_fp16 = expand_dims(axes = var_1077_axes_0, x = var_1008_cast_fp16_5)[name = string("op_1077_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_69_to_fp16, x = input_187_cast_fp16)[name = string("x_49_cast_fp16")]; - tensor var_1080_axes_0 = const()[name = string("op_1080_axes_0"), val = tensor([1])]; - tensor var_1080_cast_fp16 = expand_dims(axes = var_1080_axes_0, x = var_1008_cast_fp16_4)[name = string("op_1080_cast_fp16")]; - fp16 var_1081_to_fp16 = const()[name = string("op_1081_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1082_cast_fp16 = add(x = var_1080_cast_fp16, y = var_1081_to_fp16)[name = string("op_1082_cast_fp16")]; - tensor var_1083_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1082_cast_fp16)[name = string("op_1083_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_1008_cast_fp16_3)[name = string("op_1084_cast_fp16")]; - tensor input_189_cast_fp16 = add(x = var_1083_cast_fp16, y = var_1084_cast_fp16)[name = string("input_189_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(37323264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38371904))))[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(38376064)))]; - tensor linear_48_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_189_cast_fp16)[name = string("linear_48_cast_fp16")]; - string input_193_mode_0 = const()[name = string("input_193_mode_0"), val = string("EXACT")]; - tensor input_193_cast_fp16 = gelu(mode = input_193_mode_0, x = linear_48_cast_fp16)[name = string("input_193_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(38380224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39428864))))[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(39429952)))]; - tensor linear_49_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_193_cast_fp16)[name = string("linear_49_cast_fp16")]; - tensor var_1097_cast_fp16 = mul(x = var_1077_cast_fp16, y = linear_49_cast_fp16)[name = string("op_1097_cast_fp16")]; - tensor input_201_cast_fp16 = add(x = input_187_cast_fp16, y = var_1097_cast_fp16)[name = string("input_201_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(39431040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41003968))))[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(41010176)))]; - tensor linear_50_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_39_cast_fp16)[name = string("linear_50_cast_fp16")]; - tensor var_1106_split_sizes_0 = const()[name = string("op_1106_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1106_axis_0 = const()[name = string("op_1106_axis_0"), val = int32(-1)]; - tensor var_1106_cast_fp16_0, tensor var_1106_cast_fp16_1, tensor var_1106_cast_fp16_2, tensor var_1106_cast_fp16_3, tensor var_1106_cast_fp16_4, tensor var_1106_cast_fp16_5 = split(axis = var_1106_axis_0, split_sizes = var_1106_split_sizes_0, x = linear_50_cast_fp16)[name = string("op_1106_cast_fp16")]; - tensor var_1112_axes_0 = const()[name = string("op_1112_axes_0"), val = tensor([1])]; - tensor var_1112_cast_fp16 = expand_dims(axes = var_1112_axes_0, x = var_1106_cast_fp16_2)[name = string("op_1112_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_69_to_fp16, x = input_201_cast_fp16)[name = string("x_51_cast_fp16")]; - tensor var_1115_axes_0 = const()[name = string("op_1115_axes_0"), val = tensor([1])]; - tensor var_1115_cast_fp16 = expand_dims(axes = var_1115_axes_0, x = var_1106_cast_fp16_1)[name = string("op_1115_cast_fp16")]; - fp16 var_1116_to_fp16 = const()[name = string("op_1116_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1117_cast_fp16 = add(x = var_1115_cast_fp16, y = var_1116_to_fp16)[name = string("op_1117_cast_fp16")]; - tensor var_1118_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1117_cast_fp16)[name = string("op_1118_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_1106_cast_fp16_0)[name = string("op_1119_cast_fp16")]; - tensor x_53_cast_fp16 = add(x = var_1118_cast_fp16, y = var_1119_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(41016384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41802880))))[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(41806016)))]; - tensor linear_51_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_51_cast_fp16")]; - tensor var_1131 = const()[name = string("op_1131"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_17_cast_fp16 = reshape(shape = var_1131, x = linear_51_cast_fp16)[name = string("qkv_17_cast_fp16")]; - tensor var_1133_split_sizes_0 = const()[name = string("op_1133_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1133_axis_0 = const()[name = string("op_1133_axis_0"), val = int32(2)]; - tensor var_1133_cast_fp16_0, tensor var_1133_cast_fp16_1, tensor var_1133_cast_fp16_2 = split(axis = var_1133_axis_0, split_sizes = var_1133_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1133_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_1133_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_1133_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_1133_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; - tensor var_1138_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1138_cast_fp16")]; - tensor var_1140_axes_0 = const()[name = string("op_1140_axes_0"), val = tensor([-1])]; - bool var_1140_keep_dims_0 = const()[name = string("op_1140_keep_dims_0"), val = bool(true)]; - tensor var_1140_cast_fp16 = reduce_mean(axes = var_1140_axes_0, keep_dims = var_1140_keep_dims_0, x = var_1138_cast_fp16)[name = string("op_1140_cast_fp16")]; - fp16 var_1141_to_fp16 = const()[name = string("op_1141_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1142_cast_fp16 = add(x = var_1140_cast_fp16, y = var_1141_to_fp16)[name = string("op_1142_cast_fp16")]; - fp32 var_1143_epsilon_0 = const()[name = string("op_1143_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1143_cast_fp16 = rsqrt(epsilon = var_1143_epsilon_0, x = var_1142_cast_fp16)[name = string("op_1143_cast_fp16")]; - tensor var_1144_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1143_cast_fp16)[name = string("op_1144_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(41809152)))]; - tensor var_1145_cast_fp16 = mul(x = var_1144_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1145_cast_fp16")]; - tensor var_1148_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1148_cast_fp16")]; - tensor var_1150_axes_0 = const()[name = string("op_1150_axes_0"), val = tensor([-1])]; - bool var_1150_keep_dims_0 = const()[name = string("op_1150_keep_dims_0"), val = bool(true)]; - tensor var_1150_cast_fp16 = reduce_mean(axes = var_1150_axes_0, keep_dims = var_1150_keep_dims_0, x = var_1148_cast_fp16)[name = string("op_1150_cast_fp16")]; - fp16 var_1151_to_fp16 = const()[name = string("op_1151_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1152_cast_fp16 = add(x = var_1150_cast_fp16, y = var_1151_to_fp16)[name = string("op_1152_cast_fp16")]; - fp32 var_1153_epsilon_0 = const()[name = string("op_1153_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1153_cast_fp16 = rsqrt(epsilon = var_1153_epsilon_0, x = var_1152_cast_fp16)[name = string("op_1153_cast_fp16")]; - tensor var_1154_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1153_cast_fp16)[name = string("op_1154_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(41809344)))]; - tensor var_1155_cast_fp16 = mul(x = var_1154_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1155_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_1155_cast_fp16)[name = string("transpose_123")]; - tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1145_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_1166_perm_0 = const()[name = string("op_1166_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1167 = const()[name = string("op_1167"), val = tensor([1, 608, 512])]; - tensor var_1166_cast_fp16 = transpose(perm = var_1166_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; - tensor input_203_cast_fp16 = reshape(shape = var_1167, x = var_1166_cast_fp16)[name = string("input_203_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(41809536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42071744))))[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(42072832)))]; - tensor linear_52_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_203_cast_fp16)[name = string("linear_52_cast_fp16")]; - tensor var_1173_cast_fp16 = mul(x = var_1112_cast_fp16, y = linear_52_cast_fp16)[name = string("op_1173_cast_fp16")]; - tensor input_207_cast_fp16 = add(x = input_201_cast_fp16, y = var_1173_cast_fp16)[name = string("input_207_cast_fp16")]; - tensor var_1175_axes_0 = const()[name = string("op_1175_axes_0"), val = tensor([1])]; - tensor var_1175_cast_fp16 = expand_dims(axes = var_1175_axes_0, x = var_1106_cast_fp16_5)[name = string("op_1175_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_69_to_fp16, x = input_207_cast_fp16)[name = string("x_55_cast_fp16")]; - tensor var_1178_axes_0 = const()[name = string("op_1178_axes_0"), val = tensor([1])]; - tensor var_1178_cast_fp16 = expand_dims(axes = var_1178_axes_0, x = var_1106_cast_fp16_4)[name = string("op_1178_cast_fp16")]; - fp16 var_1179_to_fp16 = const()[name = string("op_1179_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1180_cast_fp16 = add(x = var_1178_cast_fp16, y = var_1179_to_fp16)[name = string("op_1180_cast_fp16")]; - tensor var_1181_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1180_cast_fp16)[name = string("op_1181_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_1106_cast_fp16_3)[name = string("op_1182_cast_fp16")]; - tensor input_209_cast_fp16 = add(x = var_1181_cast_fp16, y = var_1182_cast_fp16)[name = string("input_209_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(42073920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43122560))))[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(43126720)))]; - tensor linear_53_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_209_cast_fp16)[name = string("linear_53_cast_fp16")]; - string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("EXACT")]; - tensor input_213_cast_fp16 = gelu(mode = input_213_mode_0, x = linear_53_cast_fp16)[name = string("input_213_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(43130880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44179520))))[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(44180608)))]; - tensor linear_54_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_213_cast_fp16)[name = string("linear_54_cast_fp16")]; - tensor var_1195_cast_fp16 = mul(x = var_1175_cast_fp16, y = linear_54_cast_fp16)[name = string("op_1195_cast_fp16")]; - tensor input_221_cast_fp16 = add(x = input_207_cast_fp16, y = var_1195_cast_fp16)[name = string("input_221_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(44181696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45754624))))[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(45760832)))]; - tensor linear_55_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_39_cast_fp16)[name = string("linear_55_cast_fp16")]; - tensor var_1204_split_sizes_0 = const()[name = string("op_1204_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1204_axis_0 = const()[name = string("op_1204_axis_0"), val = int32(-1)]; - tensor var_1204_cast_fp16_0, tensor var_1204_cast_fp16_1, tensor var_1204_cast_fp16_2, tensor var_1204_cast_fp16_3, tensor var_1204_cast_fp16_4, tensor var_1204_cast_fp16_5 = split(axis = var_1204_axis_0, split_sizes = var_1204_split_sizes_0, x = linear_55_cast_fp16)[name = string("op_1204_cast_fp16")]; - tensor var_1210_axes_0 = const()[name = string("op_1210_axes_0"), val = tensor([1])]; - tensor var_1210_cast_fp16 = expand_dims(axes = var_1210_axes_0, x = var_1204_cast_fp16_2)[name = string("op_1210_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_69_to_fp16, x = input_221_cast_fp16)[name = string("x_57_cast_fp16")]; - tensor var_1213_axes_0 = const()[name = string("op_1213_axes_0"), val = tensor([1])]; - tensor var_1213_cast_fp16 = expand_dims(axes = var_1213_axes_0, x = var_1204_cast_fp16_1)[name = string("op_1213_cast_fp16")]; - fp16 var_1214_to_fp16 = const()[name = string("op_1214_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1215_cast_fp16 = add(x = var_1213_cast_fp16, y = var_1214_to_fp16)[name = string("op_1215_cast_fp16")]; - tensor var_1216_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1215_cast_fp16)[name = string("op_1216_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_1204_cast_fp16_0)[name = string("op_1217_cast_fp16")]; - tensor x_59_cast_fp16 = add(x = var_1216_cast_fp16, y = var_1217_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(45767040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46553536))))[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(46556672)))]; - tensor linear_56_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_56_cast_fp16")]; - tensor var_1229 = const()[name = string("op_1229"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_19_cast_fp16 = reshape(shape = var_1229, x = linear_56_cast_fp16)[name = string("qkv_19_cast_fp16")]; - tensor var_1231_split_sizes_0 = const()[name = string("op_1231_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1231_axis_0 = const()[name = string("op_1231_axis_0"), val = int32(2)]; - tensor var_1231_cast_fp16_0, tensor var_1231_cast_fp16_1, tensor var_1231_cast_fp16_2 = split(axis = var_1231_axis_0, split_sizes = var_1231_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1231_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_1231_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_1231_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_1231_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; - tensor var_1236_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1236_cast_fp16")]; - tensor var_1238_axes_0 = const()[name = string("op_1238_axes_0"), val = tensor([-1])]; - bool var_1238_keep_dims_0 = const()[name = string("op_1238_keep_dims_0"), val = bool(true)]; - tensor var_1238_cast_fp16 = reduce_mean(axes = var_1238_axes_0, keep_dims = var_1238_keep_dims_0, x = var_1236_cast_fp16)[name = string("op_1238_cast_fp16")]; - fp16 var_1239_to_fp16 = const()[name = string("op_1239_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1240_cast_fp16 = add(x = var_1238_cast_fp16, y = var_1239_to_fp16)[name = string("op_1240_cast_fp16")]; - fp32 var_1241_epsilon_0 = const()[name = string("op_1241_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1241_cast_fp16 = rsqrt(epsilon = var_1241_epsilon_0, x = var_1240_cast_fp16)[name = string("op_1241_cast_fp16")]; - tensor var_1242_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1241_cast_fp16)[name = string("op_1242_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(46559808)))]; - tensor var_1243_cast_fp16 = mul(x = var_1242_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1243_cast_fp16")]; - tensor var_1246_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1246_cast_fp16")]; - tensor var_1248_axes_0 = const()[name = string("op_1248_axes_0"), val = tensor([-1])]; - bool var_1248_keep_dims_0 = const()[name = string("op_1248_keep_dims_0"), val = bool(true)]; - tensor var_1248_cast_fp16 = reduce_mean(axes = var_1248_axes_0, keep_dims = var_1248_keep_dims_0, x = var_1246_cast_fp16)[name = string("op_1248_cast_fp16")]; - fp16 var_1249_to_fp16 = const()[name = string("op_1249_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1250_cast_fp16 = add(x = var_1248_cast_fp16, y = var_1249_to_fp16)[name = string("op_1250_cast_fp16")]; - fp32 var_1251_epsilon_0 = const()[name = string("op_1251_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1251_cast_fp16 = rsqrt(epsilon = var_1251_epsilon_0, x = var_1250_cast_fp16)[name = string("op_1251_cast_fp16")]; - tensor var_1252_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1251_cast_fp16)[name = string("op_1252_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(46560000)))]; - tensor var_1253_cast_fp16 = mul(x = var_1252_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1253_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_1253_cast_fp16)[name = string("transpose_119")]; - tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1243_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_1264_perm_0 = const()[name = string("op_1264_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1265 = const()[name = string("op_1265"), val = tensor([1, 608, 512])]; - tensor var_1264_cast_fp16 = transpose(perm = var_1264_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; - tensor input_223_cast_fp16 = reshape(shape = var_1265, x = var_1264_cast_fp16)[name = string("input_223_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(46560192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46822400))))[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(46823488)))]; - tensor linear_57_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_223_cast_fp16)[name = string("linear_57_cast_fp16")]; - tensor var_1271_cast_fp16 = mul(x = var_1210_cast_fp16, y = linear_57_cast_fp16)[name = string("op_1271_cast_fp16")]; - tensor input_227_cast_fp16 = add(x = input_221_cast_fp16, y = var_1271_cast_fp16)[name = string("input_227_cast_fp16")]; - tensor var_1273_axes_0 = const()[name = string("op_1273_axes_0"), val = tensor([1])]; - tensor var_1273_cast_fp16 = expand_dims(axes = var_1273_axes_0, x = var_1204_cast_fp16_5)[name = string("op_1273_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_69_to_fp16, x = input_227_cast_fp16)[name = string("x_61_cast_fp16")]; - tensor var_1276_axes_0 = const()[name = string("op_1276_axes_0"), val = tensor([1])]; - tensor var_1276_cast_fp16 = expand_dims(axes = var_1276_axes_0, x = var_1204_cast_fp16_4)[name = string("op_1276_cast_fp16")]; - fp16 var_1277_to_fp16 = const()[name = string("op_1277_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1278_cast_fp16 = add(x = var_1276_cast_fp16, y = var_1277_to_fp16)[name = string("op_1278_cast_fp16")]; - tensor var_1279_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1278_cast_fp16)[name = string("op_1279_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_1204_cast_fp16_3)[name = string("op_1280_cast_fp16")]; - tensor input_229_cast_fp16 = add(x = var_1279_cast_fp16, y = var_1280_cast_fp16)[name = string("input_229_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(46824576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47873216))))[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(47877376)))]; - tensor linear_58_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_229_cast_fp16)[name = string("linear_58_cast_fp16")]; - string input_233_mode_0 = const()[name = string("input_233_mode_0"), val = string("EXACT")]; - tensor input_233_cast_fp16 = gelu(mode = input_233_mode_0, x = linear_58_cast_fp16)[name = string("input_233_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(47881536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48930176))))[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(48931264)))]; - tensor linear_59_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_233_cast_fp16)[name = string("linear_59_cast_fp16")]; - tensor var_1293_cast_fp16 = mul(x = var_1273_cast_fp16, y = linear_59_cast_fp16)[name = string("op_1293_cast_fp16")]; - tensor input_241_cast_fp16 = add(x = input_227_cast_fp16, y = var_1293_cast_fp16)[name = string("input_241_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(48932352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50505280))))[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(50511488)))]; - tensor linear_60_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_39_cast_fp16)[name = string("linear_60_cast_fp16")]; - tensor var_1302_split_sizes_0 = const()[name = string("op_1302_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1302_axis_0 = const()[name = string("op_1302_axis_0"), val = int32(-1)]; - tensor var_1302_cast_fp16_0, tensor var_1302_cast_fp16_1, tensor var_1302_cast_fp16_2, tensor var_1302_cast_fp16_3, tensor var_1302_cast_fp16_4, tensor var_1302_cast_fp16_5 = split(axis = var_1302_axis_0, split_sizes = var_1302_split_sizes_0, x = linear_60_cast_fp16)[name = string("op_1302_cast_fp16")]; - tensor var_1308_axes_0 = const()[name = string("op_1308_axes_0"), val = tensor([1])]; - tensor var_1308_cast_fp16 = expand_dims(axes = var_1308_axes_0, x = var_1302_cast_fp16_2)[name = string("op_1308_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_69_to_fp16, x = input_241_cast_fp16)[name = string("x_63_cast_fp16")]; - tensor var_1311_axes_0 = const()[name = string("op_1311_axes_0"), val = tensor([1])]; - tensor var_1311_cast_fp16 = expand_dims(axes = var_1311_axes_0, x = var_1302_cast_fp16_1)[name = string("op_1311_cast_fp16")]; - fp16 var_1312_to_fp16 = const()[name = string("op_1312_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1313_cast_fp16 = add(x = var_1311_cast_fp16, y = var_1312_to_fp16)[name = string("op_1313_cast_fp16")]; - tensor var_1314_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1313_cast_fp16)[name = string("op_1314_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_1302_cast_fp16_0)[name = string("op_1315_cast_fp16")]; - tensor x_65_cast_fp16 = add(x = var_1314_cast_fp16, y = var_1315_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(50517696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51304192))))[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(51307328)))]; - tensor linear_61_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_61_cast_fp16")]; - tensor var_1327 = const()[name = string("op_1327"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_21_cast_fp16 = reshape(shape = var_1327, x = linear_61_cast_fp16)[name = string("qkv_21_cast_fp16")]; - tensor var_1329_split_sizes_0 = const()[name = string("op_1329_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1329_axis_0 = const()[name = string("op_1329_axis_0"), val = int32(2)]; - tensor var_1329_cast_fp16_0, tensor var_1329_cast_fp16_1, tensor var_1329_cast_fp16_2 = split(axis = var_1329_axis_0, split_sizes = var_1329_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1329_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_1329_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_1329_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_1329_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; - tensor var_1334_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1334_cast_fp16")]; - tensor var_1336_axes_0 = const()[name = string("op_1336_axes_0"), val = tensor([-1])]; - bool var_1336_keep_dims_0 = const()[name = string("op_1336_keep_dims_0"), val = bool(true)]; - tensor var_1336_cast_fp16 = reduce_mean(axes = var_1336_axes_0, keep_dims = var_1336_keep_dims_0, x = var_1334_cast_fp16)[name = string("op_1336_cast_fp16")]; - fp16 var_1337_to_fp16 = const()[name = string("op_1337_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1338_cast_fp16 = add(x = var_1336_cast_fp16, y = var_1337_to_fp16)[name = string("op_1338_cast_fp16")]; - fp32 var_1339_epsilon_0 = const()[name = string("op_1339_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1339_cast_fp16 = rsqrt(epsilon = var_1339_epsilon_0, x = var_1338_cast_fp16)[name = string("op_1339_cast_fp16")]; - tensor var_1340_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1339_cast_fp16)[name = string("op_1340_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(51310464)))]; - tensor var_1341_cast_fp16 = mul(x = var_1340_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1341_cast_fp16")]; - tensor var_1344_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1344_cast_fp16")]; - tensor var_1346_axes_0 = const()[name = string("op_1346_axes_0"), val = tensor([-1])]; - bool var_1346_keep_dims_0 = const()[name = string("op_1346_keep_dims_0"), val = bool(true)]; - tensor var_1346_cast_fp16 = reduce_mean(axes = var_1346_axes_0, keep_dims = var_1346_keep_dims_0, x = var_1344_cast_fp16)[name = string("op_1346_cast_fp16")]; - fp16 var_1347_to_fp16 = const()[name = string("op_1347_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1348_cast_fp16 = add(x = var_1346_cast_fp16, y = var_1347_to_fp16)[name = string("op_1348_cast_fp16")]; - fp32 var_1349_epsilon_0 = const()[name = string("op_1349_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1349_cast_fp16 = rsqrt(epsilon = var_1349_epsilon_0, x = var_1348_cast_fp16)[name = string("op_1349_cast_fp16")]; - tensor var_1350_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1349_cast_fp16)[name = string("op_1350_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(51310656)))]; - tensor var_1351_cast_fp16 = mul(x = var_1350_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1351_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_1351_cast_fp16)[name = string("transpose_115")]; - tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1341_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_1362_perm_0 = const()[name = string("op_1362_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1363 = const()[name = string("op_1363"), val = tensor([1, 608, 512])]; - tensor var_1362_cast_fp16 = transpose(perm = var_1362_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; - tensor input_243_cast_fp16 = reshape(shape = var_1363, x = var_1362_cast_fp16)[name = string("input_243_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(51310848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51573056))))[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(51574144)))]; - tensor linear_62_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_243_cast_fp16)[name = string("linear_62_cast_fp16")]; - tensor var_1369_cast_fp16 = mul(x = var_1308_cast_fp16, y = linear_62_cast_fp16)[name = string("op_1369_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_241_cast_fp16, y = var_1369_cast_fp16)[name = string("input_247_cast_fp16")]; - tensor var_1371_axes_0 = const()[name = string("op_1371_axes_0"), val = tensor([1])]; - tensor var_1371_cast_fp16 = expand_dims(axes = var_1371_axes_0, x = var_1302_cast_fp16_5)[name = string("op_1371_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_69_to_fp16, x = input_247_cast_fp16)[name = string("x_67_cast_fp16")]; - tensor var_1374_axes_0 = const()[name = string("op_1374_axes_0"), val = tensor([1])]; - tensor var_1374_cast_fp16 = expand_dims(axes = var_1374_axes_0, x = var_1302_cast_fp16_4)[name = string("op_1374_cast_fp16")]; - fp16 var_1375_to_fp16 = const()[name = string("op_1375_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1376_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1375_to_fp16)[name = string("op_1376_cast_fp16")]; - tensor var_1377_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1376_cast_fp16)[name = string("op_1377_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_1302_cast_fp16_3)[name = string("op_1378_cast_fp16")]; - tensor input_249_cast_fp16 = add(x = var_1377_cast_fp16, y = var_1378_cast_fp16)[name = string("input_249_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(51575232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52623872))))[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(52628032)))]; - tensor linear_63_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_249_cast_fp16)[name = string("linear_63_cast_fp16")]; - string input_253_mode_0 = const()[name = string("input_253_mode_0"), val = string("EXACT")]; - tensor input_253_cast_fp16 = gelu(mode = input_253_mode_0, x = linear_63_cast_fp16)[name = string("input_253_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(52632192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53680832))))[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(53681920)))]; - tensor linear_64_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_253_cast_fp16)[name = string("linear_64_cast_fp16")]; - tensor var_1391_cast_fp16 = mul(x = var_1371_cast_fp16, y = linear_64_cast_fp16)[name = string("op_1391_cast_fp16")]; - tensor input_261_cast_fp16 = add(x = input_247_cast_fp16, y = var_1391_cast_fp16)[name = string("input_261_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(53683008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55255936))))[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(55262144)))]; - tensor linear_65_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_39_cast_fp16)[name = string("linear_65_cast_fp16")]; - tensor var_1400_split_sizes_0 = const()[name = string("op_1400_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1400_axis_0 = const()[name = string("op_1400_axis_0"), val = int32(-1)]; - tensor var_1400_cast_fp16_0, tensor var_1400_cast_fp16_1, tensor var_1400_cast_fp16_2, tensor var_1400_cast_fp16_3, tensor var_1400_cast_fp16_4, tensor var_1400_cast_fp16_5 = split(axis = var_1400_axis_0, split_sizes = var_1400_split_sizes_0, x = linear_65_cast_fp16)[name = string("op_1400_cast_fp16")]; - tensor var_1406_axes_0 = const()[name = string("op_1406_axes_0"), val = tensor([1])]; - tensor var_1406_cast_fp16 = expand_dims(axes = var_1406_axes_0, x = var_1400_cast_fp16_2)[name = string("op_1406_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_69_to_fp16, x = input_261_cast_fp16)[name = string("x_69_cast_fp16")]; - tensor var_1409_axes_0 = const()[name = string("op_1409_axes_0"), val = tensor([1])]; - tensor var_1409_cast_fp16 = expand_dims(axes = var_1409_axes_0, x = var_1400_cast_fp16_1)[name = string("op_1409_cast_fp16")]; - fp16 var_1410_to_fp16 = const()[name = string("op_1410_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1411_cast_fp16 = add(x = var_1409_cast_fp16, y = var_1410_to_fp16)[name = string("op_1411_cast_fp16")]; - tensor var_1412_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1411_cast_fp16)[name = string("op_1412_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_1400_cast_fp16_0)[name = string("op_1413_cast_fp16")]; - tensor x_71_cast_fp16 = add(x = var_1412_cast_fp16, y = var_1413_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(55268352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56054848))))[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(56057984)))]; - tensor linear_66_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_66_cast_fp16")]; - tensor var_1425 = const()[name = string("op_1425"), val = tensor([1, 608, 3, 8, 64])]; - tensor qkv_23_cast_fp16 = reshape(shape = var_1425, x = linear_66_cast_fp16)[name = string("qkv_23_cast_fp16")]; - tensor var_1427_split_sizes_0 = const()[name = string("op_1427_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1427_axis_0 = const()[name = string("op_1427_axis_0"), val = int32(2)]; - tensor var_1427_cast_fp16_0, tensor var_1427_cast_fp16_1, tensor var_1427_cast_fp16_2 = split(axis = var_1427_axis_0, split_sizes = var_1427_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1427_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_1427_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_1427_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_1427_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; - tensor var_1432_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1432_cast_fp16")]; - tensor var_1434_axes_0 = const()[name = string("op_1434_axes_0"), val = tensor([-1])]; - bool var_1434_keep_dims_0 = const()[name = string("op_1434_keep_dims_0"), val = bool(true)]; - tensor var_1434_cast_fp16 = reduce_mean(axes = var_1434_axes_0, keep_dims = var_1434_keep_dims_0, x = var_1432_cast_fp16)[name = string("op_1434_cast_fp16")]; - fp16 var_1435_to_fp16 = const()[name = string("op_1435_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1436_cast_fp16 = add(x = var_1434_cast_fp16, y = var_1435_to_fp16)[name = string("op_1436_cast_fp16")]; - fp32 var_1437_epsilon_0 = const()[name = string("op_1437_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1437_cast_fp16 = rsqrt(epsilon = var_1437_epsilon_0, x = var_1436_cast_fp16)[name = string("op_1437_cast_fp16")]; - tensor var_1438_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1437_cast_fp16)[name = string("op_1438_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(56061120)))]; - tensor var_1439_cast_fp16 = mul(x = var_1438_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1439_cast_fp16")]; - tensor var_1442_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1442_cast_fp16")]; - tensor var_1444_axes_0 = const()[name = string("op_1444_axes_0"), val = tensor([-1])]; - bool var_1444_keep_dims_0 = const()[name = string("op_1444_keep_dims_0"), val = bool(true)]; - tensor var_1444_cast_fp16 = reduce_mean(axes = var_1444_axes_0, keep_dims = var_1444_keep_dims_0, x = var_1442_cast_fp16)[name = string("op_1444_cast_fp16")]; - fp16 var_1445_to_fp16 = const()[name = string("op_1445_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1446_cast_fp16 = add(x = var_1444_cast_fp16, y = var_1445_to_fp16)[name = string("op_1446_cast_fp16")]; - fp32 var_1447_epsilon_0 = const()[name = string("op_1447_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1447_cast_fp16 = rsqrt(epsilon = var_1447_epsilon_0, x = var_1446_cast_fp16)[name = string("op_1447_cast_fp16")]; - tensor var_1448_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1447_cast_fp16)[name = string("op_1448_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(56061312)))]; - tensor var_1449_cast_fp16 = mul(x = var_1448_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1449_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_1449_cast_fp16)[name = string("transpose_111")]; - tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1439_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_1460_perm_0 = const()[name = string("op_1460_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1461 = const()[name = string("op_1461"), val = tensor([1, 608, 512])]; - tensor var_1460_cast_fp16 = transpose(perm = var_1460_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; - tensor input_263_cast_fp16 = reshape(shape = var_1461, x = var_1460_cast_fp16)[name = string("input_263_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(56061504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56323712))))[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(56324800)))]; - tensor linear_67_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_263_cast_fp16)[name = string("linear_67_cast_fp16")]; - tensor var_1467_cast_fp16 = mul(x = var_1406_cast_fp16, y = linear_67_cast_fp16)[name = string("op_1467_cast_fp16")]; - tensor input_267_cast_fp16 = add(x = input_261_cast_fp16, y = var_1467_cast_fp16)[name = string("input_267_cast_fp16")]; - tensor var_1469_axes_0 = const()[name = string("op_1469_axes_0"), val = tensor([1])]; - tensor var_1469_cast_fp16 = expand_dims(axes = var_1469_axes_0, x = var_1400_cast_fp16_5)[name = string("op_1469_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_69_to_fp16, x = input_267_cast_fp16)[name = string("x_73_cast_fp16")]; - tensor var_1472_axes_0 = const()[name = string("op_1472_axes_0"), val = tensor([1])]; - tensor var_1472_cast_fp16 = expand_dims(axes = var_1472_axes_0, x = var_1400_cast_fp16_4)[name = string("op_1472_cast_fp16")]; - fp16 var_1473_to_fp16 = const()[name = string("op_1473_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1474_cast_fp16 = add(x = var_1472_cast_fp16, y = var_1473_to_fp16)[name = string("op_1474_cast_fp16")]; - tensor var_1475_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1474_cast_fp16)[name = string("op_1475_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_1400_cast_fp16_3)[name = string("op_1476_cast_fp16")]; - tensor input_269_cast_fp16 = add(x = var_1475_cast_fp16, y = var_1476_cast_fp16)[name = string("input_269_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(56325888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57374528))))[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(57378688)))]; - tensor linear_68_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_269_cast_fp16)[name = string("linear_68_cast_fp16")]; - string input_273_mode_0 = const()[name = string("input_273_mode_0"), val = string("EXACT")]; - tensor input_273_cast_fp16 = gelu(mode = input_273_mode_0, x = linear_68_cast_fp16)[name = string("input_273_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(57382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58431488))))[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(58432576)))]; - tensor linear_69_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_273_cast_fp16)[name = string("linear_69_cast_fp16")]; - tensor var_1489_cast_fp16 = mul(x = var_1469_cast_fp16, y = linear_69_cast_fp16)[name = string("op_1489_cast_fp16")]; - tensor input_281_cast_fp16 = add(x = input_267_cast_fp16, y = var_1489_cast_fp16)[name = string("input_281_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(58433664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58958016))))[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(58960128)))]; - tensor linear_70_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_39_cast_fp16)[name = string("linear_70_cast_fp16")]; - tensor var_1495_split_sizes_0 = const()[name = string("op_1495_split_sizes_0"), val = tensor([512, 512])]; - int32 var_1495_axis_0 = const()[name = string("op_1495_axis_0"), val = int32(-1)]; - tensor var_1495_cast_fp16_0, tensor var_1495_cast_fp16_1 = split(axis = var_1495_axis_0, split_sizes = var_1495_split_sizes_0, x = linear_70_cast_fp16)[name = string("op_1495_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_69_to_fp16, x = input_281_cast_fp16)[name = string("x_cast_fp16")]; - tensor var_1499_axes_0 = const()[name = string("op_1499_axes_0"), val = tensor([1])]; - tensor var_1499_cast_fp16 = expand_dims(axes = var_1499_axes_0, x = var_1495_cast_fp16_1)[name = string("op_1499_cast_fp16")]; - fp16 var_1500_to_fp16 = const()[name = string("op_1500_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1501_cast_fp16 = add(x = var_1499_cast_fp16, y = var_1500_to_fp16)[name = string("op_1501_cast_fp16")]; - tensor var_1502_cast_fp16 = mul(x = x_cast_fp16, y = var_1501_cast_fp16)[name = string("op_1502_cast_fp16")]; - tensor var_1503_axes_0 = const()[name = string("op_1503_axes_0"), val = tensor([1])]; - tensor var_1503_cast_fp16 = expand_dims(axes = var_1503_axes_0, x = var_1495_cast_fp16_0)[name = string("op_1503_cast_fp16")]; - tensor encoded_cast_fp16 = add(x = var_1502_cast_fp16, y = var_1503_cast_fp16)[name = string("encoded_cast_fp16")]; - tensor input_283_begin_0 = const()[name = string("input_283_begin_0"), val = tensor([0, 96, 0])]; - tensor input_283_end_0 = const()[name = string("input_283_end_0"), val = tensor([1, 608, 512])]; - tensor input_283_end_mask_0 = const()[name = string("input_283_end_mask_0"), val = tensor([true, true, true])]; - tensor input_283_cast_fp16 = slice_by_index(begin = input_283_begin_0, end = input_283_end_0, end_mask = input_283_end_mask_0, x = encoded_cast_fp16)[name = string("input_283_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(58962240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59224448))))[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(59225536)))]; - tensor linear_71_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_71_cast_fp16")]; - string input_287_mode_0 = const()[name = string("input_287_mode_0"), val = string("EXACT")]; - tensor input_287_cast_fp16 = gelu(mode = input_287_mode_0, x = linear_71_cast_fp16)[name = string("input_287_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(59226624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59238976))))[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(59239104)))]; - tensor linear_72_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_72_cast_fp16")]; - tensor var_1517_perm_0 = const()[name = string("op_1517_perm_0"), val = tensor([0, 2, 1])]; - string var_1517_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1517_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; - tensor var_1517_cast_fp16 = transpose(perm = var_1517_perm_0, x = linear_72_cast_fp16)[name = string("transpose_108")]; - tensor velocity = cast(dtype = var_1517_cast_fp16_to_fp32_dtype_0, x = var_1517_cast_fp16)[name = string("cast_1")]; - } -> (velocity); - func vector_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 prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { - int32 var_33 = const()[name = string("op_33"), val = int32(0)]; - tensor present = not_equal(x = delivery_present, y = var_33)[name = string("present")]; - int32 var_35 = const()[name = string("op_35"), val = int32(0)]; - tensor latent_mask_1 = not_equal(x = latent_mask, y = var_35)[name = string("latent_mask")]; - int32 var_37 = const()[name = string("op_37"), val = int32(0)]; - tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_37)[name = string("prefix_mask_1")]; - int32 var_58 = const()[name = string("op_58"), val = int32(-1)]; - int32 var_73 = const()[name = string("op_73"), val = int32(1)]; - int32 var_87 = const()[name = string("op_87"), val = int32(7)]; - tensor var_88 = mul(x = voice_id, y = var_87)[name = string("op_88")]; - tensor index = add(x = var_88, 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(35968))))[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_20")]; - tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_19")]; - 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(70)]; - 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_18")]; - tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_17")]; - 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(70)]; - 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(36224)))]; - 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_16")]; - 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(40768)))]; - 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(45312)))]; - 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(49856)))]; - 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(50112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62464))))[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(63552)))]; - tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_15")]; - 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_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_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109248))))[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_14")]; - tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; - tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; - int32 var_150_axis_0 = const()[name = string("op_150_axis_0"), val = int32(0)]; - int32 var_150_batch_dims_0 = const()[name = string("op_150_batch_dims_0"), val = int32(0)]; - bool var_150_validate_indices_0 = const()[name = string("op_150_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(109504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154624))))[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_13")]; - tensor var_150_cast_fp16_cast_uint16 = gather(axis = var_150_axis_0, batch_dims = var_150_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_150_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_150_cast_fp16_cast_uint16")]; - int32 var_155_axis_0 = const()[name = string("op_155_axis_0"), val = int32(0)]; - int32 var_155_batch_dims_0 = const()[name = string("op_155_batch_dims_0"), val = int32(0)]; - bool var_155_validate_indices_0 = const()[name = string("op_155_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(154880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187712))))[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_12")]; - tensor var_155_cast_fp16_cast_uint16 = gather(axis = var_155_axis_0, batch_dims = var_155_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_155_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_155_cast_fp16_cast_uint16")]; - tensor hidden_7_cast_fp16 = add(x = var_150_cast_fp16_cast_uint16, y = var_155_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_10")]; - 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_11")]; - 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(187904)))]; - 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(190016)))]; - 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_166_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_166_cast_fp16")]; - tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_166_cast_fp16)[name = string("hidden_11_cast_fp16")]; - int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(0)]; - int32 var_170_batch_dims_0 = const()[name = string("op_170_batch_dims_0"), val = int32(0)]; - bool var_170_validate_indices_0 = const()[name = string("op_170_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(191104)))]; - 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_9")]; - tensor var_170_cast_fp16_cast_uint16 = gather(axis = var_170_axis_0, batch_dims = var_170_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_170_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_170_cast_fp16_cast_uint16")]; - tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_170_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; - tensor op_174_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59832320))), scale = tensor([[[0x1.46cp-5]]]))[name = string("op_174_to_fp16_quantized")]; - tensor hidden_17_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_174_to_fp16_quantized)[name = string("hidden_17_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(261888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267072))))[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(267200)))]; - 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_186_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_186_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_58, interleave = input_15_interleave_0, values = (var_186_cast_fp16, present_to_fp16))[name = string("input_15_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(274432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279616))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; - 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(280704)))]; - tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; - tensor var_204_axes_0 = const()[name = string("op_204_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_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = time_to_fp16)[name = string("op_204_cast_fp16")]; - tensor var_205_to_fp16 = const()[name = string("op_205_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281792)))]; - tensor phases_1_cast_fp16 = mul(x = var_204_cast_fp16, y = var_205_to_fp16)[name = string("phases_1_cast_fp16")]; - fp16 var_207_to_fp16 = const()[name = string("op_207_to_fp16"), val = fp16(0x1.92p+2)]; - tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_207_to_fp16)[name = string("phases_cast_fp16")]; - tensor var_209_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_209_cast_fp16")]; - tensor var_210_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_210_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_58, interleave = input_21_interleave_0, values = (var_209_cast_fp16, var_210_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(282368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(544576))))[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(545664)))]; - 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(546752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808960))))[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(810048)))]; - 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_220_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_220_cast_fp16")]; - tensor var_221_cast_fp16 = add(x = var_220_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_221_cast_fp16")]; - tensor var_222_cast_fp16 = add(x = var_221_cast_fp16, y = linear_2_cast_fp16)[name = string("op_222_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(811136)))]; - tensor cond_1_cast_fp16 = add(x = var_222_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; - fp16 var_61_to_fp16 = const()[name = string("op_61_to_fp16"), val = fp16(0x0p+0)]; - fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x1p+0)]; - tensor clip_0_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; - tensor clip_1_cast_fp16 = clip(alpha = var_61_to_fp16, beta = var_62_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; - tensor var_236_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_236_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(812224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(828672))))[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(829760)))]; - 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_236_cast_fp16)[name = string("linear_5_cast_fp16")]; - tensor var_242_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_242_cast_fp16")]; - tensor var_243_axes_0 = const()[name = string("op_243_axes_0"), val = tensor([-1])]; - tensor var_243_cast_fp16 = expand_dims(axes = var_243_axes_0, x = clip_1_cast_fp16)[name = string("op_243_cast_fp16")]; - tensor var_244_cast_fp16 = mul(x = var_242_cast_fp16, y = var_243_cast_fp16)[name = string("op_244_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(830848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(847296))))[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_244_cast_fp16)[name = string("linear_6_cast_fp16")]; - tensor var_250_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_250_cast_fp16")]; - tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_250_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_260_axes_0 = const()[name = string("op_260_axes_0"), val = tensor([-1])]; - tensor var_260_cast_fp16 = expand_dims(axes = var_260_axes_0, x = mask_7_cast_fp16)[name = string("op_260_cast_fp16")]; - tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_260_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(848384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110592))))[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(1111680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1373888))))[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_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_37_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_37_cast_fp16")]; - tensor input_33_perm_0 = const()[name = string("input_33_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_33_cast_fp16 = transpose(perm = input_33_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; - tensor linear_9_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_9_cast_fp16")]; - tensor op_277_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1374976))), scale = tensor([[[0x1.278p-5]]]))[name = string("op_277_to_fp16_quantized")]; - tensor context_3_cast_fp16 = add(x = linear_9_cast_fp16, y = op_277_to_fp16_quantized)[name = string("context_3_cast_fp16")]; - tensor var_280_to_fp16 = const()[name = string("op_280_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1424192)))]; - tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_280_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_73, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_17_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_73, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; - fp16 var_288_to_fp16 = const()[name = string("op_288_to_fp16"), val = fp16(0x1p-3)]; - tensor input_41_cast_fp16 = mul(x = x_1_cast_fp16, y = var_288_to_fp16)[name = string("input_41_cast_fp16")]; - tensor input_39_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("input_39_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(1425280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2998208))))[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(3004416)))]; - tensor linear_10_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_39_cast_fp16)[name = string("linear_10_cast_fp16")]; - tensor var_322_split_sizes_0 = const()[name = string("op_322_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_322_axis_0 = const()[name = string("op_322_axis_0"), val = int32(-1)]; - tensor var_322_cast_fp16_0, tensor var_322_cast_fp16_1, tensor var_322_cast_fp16_2, tensor var_322_cast_fp16_3, tensor var_322_cast_fp16_4, tensor var_322_cast_fp16_5 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = linear_10_cast_fp16)[name = string("op_322_cast_fp16")]; - tensor var_328_axes_0 = const()[name = string("op_328_axes_0"), val = tensor([1])]; - tensor var_328_cast_fp16 = expand_dims(axes = var_328_axes_0, x = var_322_cast_fp16_2)[name = string("op_328_cast_fp16")]; - tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; - fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x1.8p-23)]; - tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_69_to_fp16, x = input_41_cast_fp16)[name = string("x_3_cast_fp16")]; - tensor var_331_axes_0 = const()[name = string("op_331_axes_0"), val = tensor([1])]; - tensor var_331_cast_fp16 = expand_dims(axes = var_331_axes_0, x = var_322_cast_fp16_1)[name = string("op_331_cast_fp16")]; - fp16 var_332_to_fp16 = const()[name = string("op_332_to_fp16"), val = fp16(0x1p+0)]; - tensor var_333_cast_fp16 = add(x = var_331_cast_fp16, y = var_332_to_fp16)[name = string("op_333_cast_fp16")]; - tensor var_334_cast_fp16 = mul(x = x_3_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_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_322_cast_fp16_0)[name = string("op_335_cast_fp16")]; - tensor x_5_cast_fp16 = add(x = var_334_cast_fp16, y = var_335_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(3010624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3797120))))[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(3800256)))]; - tensor linear_11_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_11_cast_fp16")]; - tensor var_347 = const()[name = string("op_347"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_1_cast_fp16 = reshape(shape = var_347, x = linear_11_cast_fp16)[name = string("qkv_1_cast_fp16")]; - tensor var_349_split_sizes_0 = const()[name = string("op_349_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_349_axis_0 = const()[name = string("op_349_axis_0"), val = int32(2)]; - tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1, tensor var_349_cast_fp16_2 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_349_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_349_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_349_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_349_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; - tensor var_354_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_354_cast_fp16")]; - tensor var_356_axes_0 = const()[name = string("op_356_axes_0"), val = tensor([-1])]; - bool var_356_keep_dims_0 = const()[name = string("op_356_keep_dims_0"), val = bool(true)]; - tensor var_356_cast_fp16 = reduce_mean(axes = var_356_axes_0, keep_dims = var_356_keep_dims_0, x = var_354_cast_fp16)[name = string("op_356_cast_fp16")]; - fp16 var_357_to_fp16 = const()[name = string("op_357_to_fp16"), val = fp16(0x1p-23)]; - tensor var_358_cast_fp16 = add(x = var_356_cast_fp16, y = var_357_to_fp16)[name = string("op_358_cast_fp16")]; - fp32 var_359_epsilon_0 = const()[name = string("op_359_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_359_cast_fp16 = rsqrt(epsilon = var_359_epsilon_0, x = var_358_cast_fp16)[name = string("op_359_cast_fp16")]; - tensor var_360_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_359_cast_fp16)[name = string("op_360_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(3803392)))]; - tensor var_361_cast_fp16 = mul(x = var_360_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_361_cast_fp16")]; - tensor var_364_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_364_cast_fp16")]; - tensor var_366_axes_0 = const()[name = string("op_366_axes_0"), val = tensor([-1])]; - bool var_366_keep_dims_0 = const()[name = string("op_366_keep_dims_0"), val = bool(true)]; - tensor var_366_cast_fp16 = reduce_mean(axes = var_366_axes_0, keep_dims = var_366_keep_dims_0, x = var_364_cast_fp16)[name = string("op_366_cast_fp16")]; - fp16 var_367_to_fp16 = const()[name = string("op_367_to_fp16"), val = fp16(0x1p-23)]; - tensor var_368_cast_fp16 = add(x = var_366_cast_fp16, y = var_367_to_fp16)[name = string("op_368_cast_fp16")]; - fp32 var_369_epsilon_0 = const()[name = string("op_369_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_369_cast_fp16 = rsqrt(epsilon = var_369_epsilon_0, x = var_368_cast_fp16)[name = string("op_369_cast_fp16")]; - tensor var_370_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_369_cast_fp16)[name = string("op_370_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(3803584)))]; - tensor var_371_cast_fp16 = mul(x = var_370_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_371_cast_fp16")]; - tensor var_376 = const()[name = string("op_376"), val = tensor([1, 1, 1, 864])]; - tensor var_377 = reshape(shape = var_376, x = valid_mask)[name = string("op_377")]; - tensor var_379 = logical_not(x = var_377)[name = string("op_379")]; - fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(-inf)]; - tensor var_375_to_fp16 = const()[name = string("op_375_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60225600)))]; - tensor attn_mask_1_cast_fp16 = select(a = var_68_to_fp16, b = var_375_to_fp16, cond = var_379)[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_371_cast_fp16)[name = string("transpose_155")]; - tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_361_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_382_perm_0 = const()[name = string("op_382_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_383 = const()[name = string("op_383"), val = tensor([1, 864, 512])]; - tensor var_382_cast_fp16 = transpose(perm = var_382_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; - tensor input_43_cast_fp16 = reshape(shape = var_383, x = var_382_cast_fp16)[name = string("input_43_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(3804288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4066496))))[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(4067584)))]; - tensor linear_12_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_43_cast_fp16)[name = string("linear_12_cast_fp16")]; - tensor var_389_cast_fp16 = mul(x = var_328_cast_fp16, y = linear_12_cast_fp16)[name = string("op_389_cast_fp16")]; - tensor input_47_cast_fp16 = add(x = input_41_cast_fp16, y = var_389_cast_fp16)[name = string("input_47_cast_fp16")]; - tensor var_391_axes_0 = const()[name = string("op_391_axes_0"), val = tensor([1])]; - tensor var_391_cast_fp16 = expand_dims(axes = var_391_axes_0, x = var_322_cast_fp16_5)[name = string("op_391_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_69_to_fp16, x = input_47_cast_fp16)[name = string("x_7_cast_fp16")]; - tensor var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor([1])]; - tensor var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_322_cast_fp16_4)[name = string("op_394_cast_fp16")]; - fp16 var_395_to_fp16 = const()[name = string("op_395_to_fp16"), val = fp16(0x1p+0)]; - tensor var_396_cast_fp16 = add(x = var_394_cast_fp16, y = var_395_to_fp16)[name = string("op_396_cast_fp16")]; - tensor var_397_cast_fp16 = mul(x = x_7_cast_fp16, y = var_396_cast_fp16)[name = string("op_397_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_322_cast_fp16_3)[name = string("op_398_cast_fp16")]; - tensor input_49_cast_fp16 = add(x = var_397_cast_fp16, y = var_398_cast_fp16)[name = string("input_49_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(4068672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5117312))))[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(5121472)))]; - tensor linear_13_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_49_cast_fp16)[name = string("linear_13_cast_fp16")]; - string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; - tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_13_cast_fp16)[name = string("input_53_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(5125632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6174272))))[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(6175360)))]; - tensor linear_14_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_53_cast_fp16)[name = string("linear_14_cast_fp16")]; - tensor var_411_cast_fp16 = mul(x = var_391_cast_fp16, y = linear_14_cast_fp16)[name = string("op_411_cast_fp16")]; - tensor input_61_cast_fp16 = add(x = input_47_cast_fp16, y = var_411_cast_fp16)[name = string("input_61_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(6176448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7749376))))[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(7755584)))]; - tensor linear_15_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_39_cast_fp16)[name = string("linear_15_cast_fp16")]; - tensor var_420_split_sizes_0 = const()[name = string("op_420_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_420_axis_0 = const()[name = string("op_420_axis_0"), val = int32(-1)]; - tensor var_420_cast_fp16_0, tensor var_420_cast_fp16_1, tensor var_420_cast_fp16_2, tensor var_420_cast_fp16_3, tensor var_420_cast_fp16_4, tensor var_420_cast_fp16_5 = split(axis = var_420_axis_0, split_sizes = var_420_split_sizes_0, x = linear_15_cast_fp16)[name = string("op_420_cast_fp16")]; - tensor var_426_axes_0 = const()[name = string("op_426_axes_0"), val = tensor([1])]; - tensor var_426_cast_fp16 = expand_dims(axes = var_426_axes_0, x = var_420_cast_fp16_2)[name = string("op_426_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_69_to_fp16, x = input_61_cast_fp16)[name = string("x_9_cast_fp16")]; - tensor var_429_axes_0 = const()[name = string("op_429_axes_0"), val = tensor([1])]; - tensor var_429_cast_fp16 = expand_dims(axes = var_429_axes_0, x = var_420_cast_fp16_1)[name = string("op_429_cast_fp16")]; - fp16 var_430_to_fp16 = const()[name = string("op_430_to_fp16"), val = fp16(0x1p+0)]; - tensor var_431_cast_fp16 = add(x = var_429_cast_fp16, y = var_430_to_fp16)[name = string("op_431_cast_fp16")]; - tensor var_432_cast_fp16 = mul(x = x_9_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_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_420_cast_fp16_0)[name = string("op_433_cast_fp16")]; - tensor x_11_cast_fp16 = add(x = var_432_cast_fp16, y = var_433_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(7761792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548288))))[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(8551424)))]; - tensor linear_16_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_16_cast_fp16")]; - tensor var_445 = const()[name = string("op_445"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_3_cast_fp16 = reshape(shape = var_445, x = linear_16_cast_fp16)[name = string("qkv_3_cast_fp16")]; - tensor var_447_split_sizes_0 = const()[name = string("op_447_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_447_axis_0 = const()[name = string("op_447_axis_0"), val = int32(2)]; - tensor var_447_cast_fp16_0, tensor var_447_cast_fp16_1, tensor var_447_cast_fp16_2 = split(axis = var_447_axis_0, split_sizes = var_447_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_447_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_447_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_447_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_447_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; - tensor var_452_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_452_cast_fp16")]; - tensor var_454_axes_0 = const()[name = string("op_454_axes_0"), val = tensor([-1])]; - bool var_454_keep_dims_0 = const()[name = string("op_454_keep_dims_0"), val = bool(true)]; - tensor var_454_cast_fp16 = reduce_mean(axes = var_454_axes_0, keep_dims = var_454_keep_dims_0, x = var_452_cast_fp16)[name = string("op_454_cast_fp16")]; - fp16 var_455_to_fp16 = const()[name = string("op_455_to_fp16"), val = fp16(0x1p-23)]; - tensor var_456_cast_fp16 = add(x = var_454_cast_fp16, y = var_455_to_fp16)[name = string("op_456_cast_fp16")]; - fp32 var_457_epsilon_0 = const()[name = string("op_457_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_457_cast_fp16 = rsqrt(epsilon = var_457_epsilon_0, x = var_456_cast_fp16)[name = string("op_457_cast_fp16")]; - tensor var_458_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_457_cast_fp16)[name = string("op_458_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(8554560)))]; - tensor var_459_cast_fp16 = mul(x = var_458_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_459_cast_fp16")]; - tensor var_462_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_462_cast_fp16")]; - tensor var_464_axes_0 = const()[name = string("op_464_axes_0"), val = tensor([-1])]; - bool var_464_keep_dims_0 = const()[name = string("op_464_keep_dims_0"), val = bool(true)]; - tensor var_464_cast_fp16 = reduce_mean(axes = var_464_axes_0, keep_dims = var_464_keep_dims_0, x = var_462_cast_fp16)[name = string("op_464_cast_fp16")]; - fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1p-23)]; - tensor var_466_cast_fp16 = add(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("op_466_cast_fp16")]; - fp32 var_467_epsilon_0 = const()[name = string("op_467_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_467_cast_fp16 = rsqrt(epsilon = var_467_epsilon_0, x = var_466_cast_fp16)[name = string("op_467_cast_fp16")]; - tensor var_468_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_467_cast_fp16)[name = string("op_468_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(8554752)))]; - tensor var_469_cast_fp16 = mul(x = var_468_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_469_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_469_cast_fp16)[name = string("transpose_151")]; - tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_459_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_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, 864, 512])]; - tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; - tensor input_63_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("input_63_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(8554944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8817152))))[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(8818240)))]; - tensor linear_17_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_63_cast_fp16)[name = string("linear_17_cast_fp16")]; - tensor var_487_cast_fp16 = mul(x = var_426_cast_fp16, y = linear_17_cast_fp16)[name = string("op_487_cast_fp16")]; - tensor input_67_cast_fp16 = add(x = input_61_cast_fp16, y = var_487_cast_fp16)[name = string("input_67_cast_fp16")]; - tensor var_489_axes_0 = const()[name = string("op_489_axes_0"), val = tensor([1])]; - tensor var_489_cast_fp16 = expand_dims(axes = var_489_axes_0, x = var_420_cast_fp16_5)[name = string("op_489_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_69_to_fp16, x = input_67_cast_fp16)[name = string("x_13_cast_fp16")]; - tensor var_492_axes_0 = const()[name = string("op_492_axes_0"), val = tensor([1])]; - tensor var_492_cast_fp16 = expand_dims(axes = var_492_axes_0, x = var_420_cast_fp16_4)[name = string("op_492_cast_fp16")]; - fp16 var_493_to_fp16 = const()[name = string("op_493_to_fp16"), val = fp16(0x1p+0)]; - tensor var_494_cast_fp16 = add(x = var_492_cast_fp16, y = var_493_to_fp16)[name = string("op_494_cast_fp16")]; - tensor var_495_cast_fp16 = mul(x = x_13_cast_fp16, y = var_494_cast_fp16)[name = string("op_495_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_420_cast_fp16_3)[name = string("op_496_cast_fp16")]; - tensor input_69_cast_fp16 = add(x = var_495_cast_fp16, y = var_496_cast_fp16)[name = string("input_69_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(8819328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867968))))[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(9872128)))]; - tensor linear_18_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_69_cast_fp16)[name = string("linear_18_cast_fp16")]; - string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")]; - tensor input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = linear_18_cast_fp16)[name = string("input_73_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(9876288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10924928))))[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(10926016)))]; - tensor linear_19_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_73_cast_fp16)[name = string("linear_19_cast_fp16")]; - tensor var_509_cast_fp16 = mul(x = var_489_cast_fp16, y = linear_19_cast_fp16)[name = string("op_509_cast_fp16")]; - tensor input_81_cast_fp16 = add(x = input_67_cast_fp16, y = var_509_cast_fp16)[name = string("input_81_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(10927104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12500032))))[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(12506240)))]; - tensor linear_20_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_39_cast_fp16)[name = string("linear_20_cast_fp16")]; - tensor var_518_split_sizes_0 = const()[name = string("op_518_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_518_axis_0 = const()[name = string("op_518_axis_0"), val = int32(-1)]; - tensor var_518_cast_fp16_0, tensor var_518_cast_fp16_1, tensor var_518_cast_fp16_2, tensor var_518_cast_fp16_3, tensor var_518_cast_fp16_4, tensor var_518_cast_fp16_5 = split(axis = var_518_axis_0, split_sizes = var_518_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_518_cast_fp16")]; - tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; - tensor var_524_cast_fp16 = expand_dims(axes = var_524_axes_0, x = var_518_cast_fp16_2)[name = string("op_524_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_69_to_fp16, x = input_81_cast_fp16)[name = string("x_15_cast_fp16")]; - tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([1])]; - tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = var_518_cast_fp16_1)[name = string("op_527_cast_fp16")]; - fp16 var_528_to_fp16 = const()[name = string("op_528_to_fp16"), val = fp16(0x1p+0)]; - tensor var_529_cast_fp16 = add(x = var_527_cast_fp16, y = var_528_to_fp16)[name = string("op_529_cast_fp16")]; - tensor var_530_cast_fp16 = mul(x = x_15_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_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_518_cast_fp16_0)[name = string("op_531_cast_fp16")]; - tensor x_17_cast_fp16 = add(x = var_530_cast_fp16, y = var_531_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(12512448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13298944))))[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(13302080)))]; - tensor linear_21_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_21_cast_fp16")]; - tensor var_543 = const()[name = string("op_543"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_5_cast_fp16 = reshape(shape = var_543, x = linear_21_cast_fp16)[name = string("qkv_5_cast_fp16")]; - tensor var_545_split_sizes_0 = const()[name = string("op_545_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_545_axis_0 = const()[name = string("op_545_axis_0"), val = int32(2)]; - tensor var_545_cast_fp16_0, tensor var_545_cast_fp16_1, tensor var_545_cast_fp16_2 = split(axis = var_545_axis_0, split_sizes = var_545_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_545_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_545_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_545_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_545_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; - tensor var_550_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_550_cast_fp16")]; - tensor var_552_axes_0 = const()[name = string("op_552_axes_0"), val = tensor([-1])]; - bool var_552_keep_dims_0 = const()[name = string("op_552_keep_dims_0"), val = bool(true)]; - tensor var_552_cast_fp16 = reduce_mean(axes = var_552_axes_0, keep_dims = var_552_keep_dims_0, x = var_550_cast_fp16)[name = string("op_552_cast_fp16")]; - fp16 var_553_to_fp16 = const()[name = string("op_553_to_fp16"), val = fp16(0x1p-23)]; - tensor var_554_cast_fp16 = add(x = var_552_cast_fp16, y = var_553_to_fp16)[name = string("op_554_cast_fp16")]; - fp32 var_555_epsilon_0 = const()[name = string("op_555_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_555_cast_fp16 = rsqrt(epsilon = var_555_epsilon_0, x = var_554_cast_fp16)[name = string("op_555_cast_fp16")]; - tensor var_556_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_555_cast_fp16)[name = string("op_556_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(13305216)))]; - tensor var_557_cast_fp16 = mul(x = var_556_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_557_cast_fp16")]; - tensor var_560_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_560_cast_fp16")]; - tensor var_562_axes_0 = const()[name = string("op_562_axes_0"), val = tensor([-1])]; - bool var_562_keep_dims_0 = const()[name = string("op_562_keep_dims_0"), val = bool(true)]; - tensor var_562_cast_fp16 = reduce_mean(axes = var_562_axes_0, keep_dims = var_562_keep_dims_0, x = var_560_cast_fp16)[name = string("op_562_cast_fp16")]; - fp16 var_563_to_fp16 = const()[name = string("op_563_to_fp16"), val = fp16(0x1p-23)]; - tensor var_564_cast_fp16 = add(x = var_562_cast_fp16, y = var_563_to_fp16)[name = string("op_564_cast_fp16")]; - fp32 var_565_epsilon_0 = const()[name = string("op_565_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_565_cast_fp16 = rsqrt(epsilon = var_565_epsilon_0, x = var_564_cast_fp16)[name = string("op_565_cast_fp16")]; - tensor var_566_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_565_cast_fp16)[name = string("op_566_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(13305408)))]; - tensor var_567_cast_fp16 = mul(x = var_566_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_567_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_567_cast_fp16)[name = string("transpose_147")]; - tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_557_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_578_perm_0 = const()[name = string("op_578_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_579 = const()[name = string("op_579"), val = tensor([1, 864, 512])]; - tensor var_578_cast_fp16 = transpose(perm = var_578_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; - tensor input_83_cast_fp16 = reshape(shape = var_579, x = var_578_cast_fp16)[name = string("input_83_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(13305600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13567808))))[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(13568896)))]; - tensor linear_22_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_83_cast_fp16)[name = string("linear_22_cast_fp16")]; - tensor var_585_cast_fp16 = mul(x = var_524_cast_fp16, y = linear_22_cast_fp16)[name = string("op_585_cast_fp16")]; - tensor input_87_cast_fp16 = add(x = input_81_cast_fp16, y = var_585_cast_fp16)[name = string("input_87_cast_fp16")]; - tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([1])]; - tensor var_587_cast_fp16 = expand_dims(axes = var_587_axes_0, x = var_518_cast_fp16_5)[name = string("op_587_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_69_to_fp16, x = input_87_cast_fp16)[name = string("x_19_cast_fp16")]; - tensor var_590_axes_0 = const()[name = string("op_590_axes_0"), val = tensor([1])]; - tensor var_590_cast_fp16 = expand_dims(axes = var_590_axes_0, x = var_518_cast_fp16_4)[name = string("op_590_cast_fp16")]; - fp16 var_591_to_fp16 = const()[name = string("op_591_to_fp16"), val = fp16(0x1p+0)]; - tensor var_592_cast_fp16 = add(x = var_590_cast_fp16, y = var_591_to_fp16)[name = string("op_592_cast_fp16")]; - tensor var_593_cast_fp16 = mul(x = x_19_cast_fp16, y = var_592_cast_fp16)[name = string("op_593_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_518_cast_fp16_3)[name = string("op_594_cast_fp16")]; - tensor input_89_cast_fp16 = add(x = var_593_cast_fp16, y = var_594_cast_fp16)[name = string("input_89_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(13569984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14618624))))[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(14622784)))]; - tensor linear_23_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_89_cast_fp16)[name = string("linear_23_cast_fp16")]; - string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")]; - tensor input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = linear_23_cast_fp16)[name = string("input_93_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(14626944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15675584))))[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(15676672)))]; - tensor linear_24_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_93_cast_fp16)[name = string("linear_24_cast_fp16")]; - tensor var_607_cast_fp16 = mul(x = var_587_cast_fp16, y = linear_24_cast_fp16)[name = string("op_607_cast_fp16")]; - tensor input_101_cast_fp16 = add(x = input_87_cast_fp16, y = var_607_cast_fp16)[name = string("input_101_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(15677760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17250688))))[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(17256896)))]; - tensor linear_25_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_39_cast_fp16)[name = string("linear_25_cast_fp16")]; - tensor var_616_split_sizes_0 = const()[name = string("op_616_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_616_axis_0 = const()[name = string("op_616_axis_0"), val = int32(-1)]; - tensor var_616_cast_fp16_0, tensor var_616_cast_fp16_1, tensor var_616_cast_fp16_2, tensor var_616_cast_fp16_3, tensor var_616_cast_fp16_4, tensor var_616_cast_fp16_5 = split(axis = var_616_axis_0, split_sizes = var_616_split_sizes_0, x = linear_25_cast_fp16)[name = string("op_616_cast_fp16")]; - tensor var_622_axes_0 = const()[name = string("op_622_axes_0"), val = tensor([1])]; - tensor var_622_cast_fp16 = expand_dims(axes = var_622_axes_0, x = var_616_cast_fp16_2)[name = string("op_622_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_69_to_fp16, x = input_101_cast_fp16)[name = string("x_21_cast_fp16")]; - tensor var_625_axes_0 = const()[name = string("op_625_axes_0"), val = tensor([1])]; - tensor var_625_cast_fp16 = expand_dims(axes = var_625_axes_0, x = var_616_cast_fp16_1)[name = string("op_625_cast_fp16")]; - fp16 var_626_to_fp16 = const()[name = string("op_626_to_fp16"), val = fp16(0x1p+0)]; - tensor var_627_cast_fp16 = add(x = var_625_cast_fp16, y = var_626_to_fp16)[name = string("op_627_cast_fp16")]; - tensor var_628_cast_fp16 = mul(x = x_21_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_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_616_cast_fp16_0)[name = string("op_629_cast_fp16")]; - tensor x_23_cast_fp16 = add(x = var_628_cast_fp16, y = var_629_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(17263104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18049600))))[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(18052736)))]; - tensor linear_26_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_26_cast_fp16")]; - tensor var_641 = const()[name = string("op_641"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_7_cast_fp16 = reshape(shape = var_641, x = linear_26_cast_fp16)[name = string("qkv_7_cast_fp16")]; - tensor var_643_split_sizes_0 = const()[name = string("op_643_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_643_axis_0 = const()[name = string("op_643_axis_0"), val = int32(2)]; - tensor var_643_cast_fp16_0, tensor var_643_cast_fp16_1, tensor var_643_cast_fp16_2 = split(axis = var_643_axis_0, split_sizes = var_643_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_643_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_643_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_643_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_643_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; - tensor var_648_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_648_cast_fp16")]; - tensor var_650_axes_0 = const()[name = string("op_650_axes_0"), val = tensor([-1])]; - bool var_650_keep_dims_0 = const()[name = string("op_650_keep_dims_0"), val = bool(true)]; - tensor var_650_cast_fp16 = reduce_mean(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = string("op_650_cast_fp16")]; - fp16 var_651_to_fp16 = const()[name = string("op_651_to_fp16"), val = fp16(0x1p-23)]; - tensor var_652_cast_fp16 = add(x = var_650_cast_fp16, y = var_651_to_fp16)[name = string("op_652_cast_fp16")]; - fp32 var_653_epsilon_0 = const()[name = string("op_653_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_653_cast_fp16 = rsqrt(epsilon = var_653_epsilon_0, x = var_652_cast_fp16)[name = string("op_653_cast_fp16")]; - tensor var_654_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_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(18055872)))]; - tensor var_655_cast_fp16 = mul(x = var_654_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_655_cast_fp16")]; - tensor var_658_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_658_cast_fp16")]; - tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; - bool var_660_keep_dims_0 = const()[name = string("op_660_keep_dims_0"), val = bool(true)]; - tensor var_660_cast_fp16 = reduce_mean(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = string("op_660_cast_fp16")]; - fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-23)]; - tensor var_662_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("op_662_cast_fp16")]; - fp32 var_663_epsilon_0 = const()[name = string("op_663_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_663_cast_fp16 = rsqrt(epsilon = var_663_epsilon_0, x = var_662_cast_fp16)[name = string("op_663_cast_fp16")]; - tensor var_664_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_663_cast_fp16)[name = string("op_664_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(18056064)))]; - tensor var_665_cast_fp16 = mul(x = var_664_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_665_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_665_cast_fp16)[name = string("transpose_143")]; - tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_655_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_676_perm_0 = const()[name = string("op_676_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_677 = const()[name = string("op_677"), val = tensor([1, 864, 512])]; - tensor var_676_cast_fp16 = transpose(perm = var_676_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; - tensor input_103_cast_fp16 = reshape(shape = var_677, x = var_676_cast_fp16)[name = string("input_103_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(18056256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18318464))))[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(18319552)))]; - tensor linear_27_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_103_cast_fp16)[name = string("linear_27_cast_fp16")]; - tensor var_683_cast_fp16 = mul(x = var_622_cast_fp16, y = linear_27_cast_fp16)[name = string("op_683_cast_fp16")]; - tensor input_107_cast_fp16 = add(x = input_101_cast_fp16, y = var_683_cast_fp16)[name = string("input_107_cast_fp16")]; - tensor var_685_axes_0 = const()[name = string("op_685_axes_0"), val = tensor([1])]; - tensor var_685_cast_fp16 = expand_dims(axes = var_685_axes_0, x = var_616_cast_fp16_5)[name = string("op_685_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_69_to_fp16, x = input_107_cast_fp16)[name = string("x_25_cast_fp16")]; - tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([1])]; - tensor var_688_cast_fp16 = expand_dims(axes = var_688_axes_0, x = var_616_cast_fp16_4)[name = string("op_688_cast_fp16")]; - fp16 var_689_to_fp16 = const()[name = string("op_689_to_fp16"), val = fp16(0x1p+0)]; - tensor var_690_cast_fp16 = add(x = var_688_cast_fp16, y = var_689_to_fp16)[name = string("op_690_cast_fp16")]; - tensor var_691_cast_fp16 = mul(x = x_25_cast_fp16, y = var_690_cast_fp16)[name = string("op_691_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_616_cast_fp16_3)[name = string("op_692_cast_fp16")]; - tensor input_109_cast_fp16 = add(x = var_691_cast_fp16, y = var_692_cast_fp16)[name = string("input_109_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(18320640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19369280))))[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(19373440)))]; - tensor linear_28_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_109_cast_fp16)[name = string("linear_28_cast_fp16")]; - string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; - tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_28_cast_fp16)[name = string("input_113_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(19377600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20426240))))[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(20427328)))]; - tensor linear_29_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_113_cast_fp16)[name = string("linear_29_cast_fp16")]; - tensor var_705_cast_fp16 = mul(x = var_685_cast_fp16, y = linear_29_cast_fp16)[name = string("op_705_cast_fp16")]; - tensor input_121_cast_fp16 = add(x = input_107_cast_fp16, y = var_705_cast_fp16)[name = string("input_121_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(20428416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001344))))[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(22007552)))]; - tensor linear_30_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_39_cast_fp16)[name = string("linear_30_cast_fp16")]; - tensor var_714_split_sizes_0 = const()[name = string("op_714_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_714_axis_0 = const()[name = string("op_714_axis_0"), val = int32(-1)]; - tensor var_714_cast_fp16_0, tensor var_714_cast_fp16_1, tensor var_714_cast_fp16_2, tensor var_714_cast_fp16_3, tensor var_714_cast_fp16_4, tensor var_714_cast_fp16_5 = split(axis = var_714_axis_0, split_sizes = var_714_split_sizes_0, x = linear_30_cast_fp16)[name = string("op_714_cast_fp16")]; - tensor var_720_axes_0 = const()[name = string("op_720_axes_0"), val = tensor([1])]; - tensor var_720_cast_fp16 = expand_dims(axes = var_720_axes_0, x = var_714_cast_fp16_2)[name = string("op_720_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_69_to_fp16, x = input_121_cast_fp16)[name = string("x_27_cast_fp16")]; - tensor var_723_axes_0 = const()[name = string("op_723_axes_0"), val = tensor([1])]; - tensor var_723_cast_fp16 = expand_dims(axes = var_723_axes_0, x = var_714_cast_fp16_1)[name = string("op_723_cast_fp16")]; - fp16 var_724_to_fp16 = const()[name = string("op_724_to_fp16"), val = fp16(0x1p+0)]; - tensor var_725_cast_fp16 = add(x = var_723_cast_fp16, y = var_724_to_fp16)[name = string("op_725_cast_fp16")]; - tensor var_726_cast_fp16 = mul(x = x_27_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_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_714_cast_fp16_0)[name = string("op_727_cast_fp16")]; - tensor x_29_cast_fp16 = add(x = var_726_cast_fp16, y = var_727_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(22013760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22800256))))[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(22803392)))]; - tensor linear_31_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_31_cast_fp16")]; - tensor var_739 = const()[name = string("op_739"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_9_cast_fp16 = reshape(shape = var_739, x = linear_31_cast_fp16)[name = string("qkv_9_cast_fp16")]; - tensor var_741_split_sizes_0 = const()[name = string("op_741_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_741_axis_0 = const()[name = string("op_741_axis_0"), val = int32(2)]; - tensor var_741_cast_fp16_0, tensor var_741_cast_fp16_1, tensor var_741_cast_fp16_2 = split(axis = var_741_axis_0, split_sizes = var_741_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_741_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_741_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_741_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_741_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; - tensor var_746_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_746_cast_fp16")]; - tensor var_748_axes_0 = const()[name = string("op_748_axes_0"), val = tensor([-1])]; - bool var_748_keep_dims_0 = const()[name = string("op_748_keep_dims_0"), val = bool(true)]; - tensor var_748_cast_fp16 = reduce_mean(axes = var_748_axes_0, keep_dims = var_748_keep_dims_0, x = var_746_cast_fp16)[name = string("op_748_cast_fp16")]; - fp16 var_749_to_fp16 = const()[name = string("op_749_to_fp16"), val = fp16(0x1p-23)]; - tensor var_750_cast_fp16 = add(x = var_748_cast_fp16, y = var_749_to_fp16)[name = string("op_750_cast_fp16")]; - fp32 var_751_epsilon_0 = const()[name = string("op_751_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_751_cast_fp16 = rsqrt(epsilon = var_751_epsilon_0, x = var_750_cast_fp16)[name = string("op_751_cast_fp16")]; - tensor var_752_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_751_cast_fp16)[name = string("op_752_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(22806528)))]; - tensor var_753_cast_fp16 = mul(x = var_752_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_753_cast_fp16")]; - tensor var_756_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_756_cast_fp16")]; - tensor var_758_axes_0 = const()[name = string("op_758_axes_0"), val = tensor([-1])]; - bool var_758_keep_dims_0 = const()[name = string("op_758_keep_dims_0"), val = bool(true)]; - tensor var_758_cast_fp16 = reduce_mean(axes = var_758_axes_0, keep_dims = var_758_keep_dims_0, x = var_756_cast_fp16)[name = string("op_758_cast_fp16")]; - fp16 var_759_to_fp16 = const()[name = string("op_759_to_fp16"), val = fp16(0x1p-23)]; - tensor var_760_cast_fp16 = add(x = var_758_cast_fp16, y = var_759_to_fp16)[name = string("op_760_cast_fp16")]; - fp32 var_761_epsilon_0 = const()[name = string("op_761_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_761_cast_fp16 = rsqrt(epsilon = var_761_epsilon_0, x = var_760_cast_fp16)[name = string("op_761_cast_fp16")]; - tensor var_762_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_761_cast_fp16)[name = string("op_762_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(22806720)))]; - tensor var_763_cast_fp16 = mul(x = var_762_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_763_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_763_cast_fp16)[name = string("transpose_139")]; - tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_753_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_774_perm_0 = const()[name = string("op_774_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_775 = const()[name = string("op_775"), val = tensor([1, 864, 512])]; - tensor var_774_cast_fp16 = transpose(perm = var_774_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; - tensor input_123_cast_fp16 = reshape(shape = var_775, x = var_774_cast_fp16)[name = string("input_123_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(22806912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23069120))))[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(23070208)))]; - tensor linear_32_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_123_cast_fp16)[name = string("linear_32_cast_fp16")]; - tensor var_781_cast_fp16 = mul(x = var_720_cast_fp16, y = linear_32_cast_fp16)[name = string("op_781_cast_fp16")]; - tensor input_127_cast_fp16 = add(x = input_121_cast_fp16, y = var_781_cast_fp16)[name = string("input_127_cast_fp16")]; - tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([1])]; - tensor var_783_cast_fp16 = expand_dims(axes = var_783_axes_0, x = var_714_cast_fp16_5)[name = string("op_783_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_69_to_fp16, x = input_127_cast_fp16)[name = string("x_31_cast_fp16")]; - tensor var_786_axes_0 = const()[name = string("op_786_axes_0"), val = tensor([1])]; - tensor var_786_cast_fp16 = expand_dims(axes = var_786_axes_0, x = var_714_cast_fp16_4)[name = string("op_786_cast_fp16")]; - fp16 var_787_to_fp16 = const()[name = string("op_787_to_fp16"), val = fp16(0x1p+0)]; - tensor var_788_cast_fp16 = add(x = var_786_cast_fp16, y = var_787_to_fp16)[name = string("op_788_cast_fp16")]; - tensor var_789_cast_fp16 = mul(x = x_31_cast_fp16, y = var_788_cast_fp16)[name = string("op_789_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_714_cast_fp16_3)[name = string("op_790_cast_fp16")]; - tensor input_129_cast_fp16 = add(x = var_789_cast_fp16, y = var_790_cast_fp16)[name = string("input_129_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(23071296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119936))))[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(24124096)))]; - tensor linear_33_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_129_cast_fp16)[name = string("linear_33_cast_fp16")]; - string input_133_mode_0 = const()[name = string("input_133_mode_0"), val = string("EXACT")]; - tensor input_133_cast_fp16 = gelu(mode = input_133_mode_0, x = linear_33_cast_fp16)[name = string("input_133_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(24128256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176896))))[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(25177984)))]; - tensor linear_34_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_133_cast_fp16)[name = string("linear_34_cast_fp16")]; - tensor var_803_cast_fp16 = mul(x = var_783_cast_fp16, y = linear_34_cast_fp16)[name = string("op_803_cast_fp16")]; - tensor input_141_cast_fp16 = add(x = input_127_cast_fp16, y = var_803_cast_fp16)[name = string("input_141_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(25179072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26752000))))[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(26758208)))]; - tensor linear_35_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_39_cast_fp16)[name = string("linear_35_cast_fp16")]; - tensor var_812_split_sizes_0 = const()[name = string("op_812_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_812_axis_0 = const()[name = string("op_812_axis_0"), val = int32(-1)]; - tensor var_812_cast_fp16_0, tensor var_812_cast_fp16_1, tensor var_812_cast_fp16_2, tensor var_812_cast_fp16_3, tensor var_812_cast_fp16_4, tensor var_812_cast_fp16_5 = split(axis = var_812_axis_0, split_sizes = var_812_split_sizes_0, x = linear_35_cast_fp16)[name = string("op_812_cast_fp16")]; - tensor var_818_axes_0 = const()[name = string("op_818_axes_0"), val = tensor([1])]; - tensor var_818_cast_fp16 = expand_dims(axes = var_818_axes_0, x = var_812_cast_fp16_2)[name = string("op_818_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_69_to_fp16, x = input_141_cast_fp16)[name = string("x_33_cast_fp16")]; - tensor var_821_axes_0 = const()[name = string("op_821_axes_0"), val = tensor([1])]; - tensor var_821_cast_fp16 = expand_dims(axes = var_821_axes_0, x = var_812_cast_fp16_1)[name = string("op_821_cast_fp16")]; - fp16 var_822_to_fp16 = const()[name = string("op_822_to_fp16"), val = fp16(0x1p+0)]; - tensor var_823_cast_fp16 = add(x = var_821_cast_fp16, y = var_822_to_fp16)[name = string("op_823_cast_fp16")]; - tensor var_824_cast_fp16 = mul(x = x_33_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_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_812_cast_fp16_0)[name = string("op_825_cast_fp16")]; - tensor x_35_cast_fp16 = add(x = var_824_cast_fp16, y = var_825_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(26764416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27550912))))[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(27554048)))]; - tensor linear_36_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_36_cast_fp16")]; - tensor var_837 = const()[name = string("op_837"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_11_cast_fp16 = reshape(shape = var_837, x = linear_36_cast_fp16)[name = string("qkv_11_cast_fp16")]; - tensor var_839_split_sizes_0 = const()[name = string("op_839_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_839_axis_0 = const()[name = string("op_839_axis_0"), val = int32(2)]; - tensor var_839_cast_fp16_0, tensor var_839_cast_fp16_1, tensor var_839_cast_fp16_2 = split(axis = var_839_axis_0, split_sizes = var_839_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_839_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_839_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_839_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_839_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; - tensor var_844_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_844_cast_fp16")]; - tensor var_846_axes_0 = const()[name = string("op_846_axes_0"), val = tensor([-1])]; - bool var_846_keep_dims_0 = const()[name = string("op_846_keep_dims_0"), val = bool(true)]; - tensor var_846_cast_fp16 = reduce_mean(axes = var_846_axes_0, keep_dims = var_846_keep_dims_0, x = var_844_cast_fp16)[name = string("op_846_cast_fp16")]; - fp16 var_847_to_fp16 = const()[name = string("op_847_to_fp16"), val = fp16(0x1p-23)]; - tensor var_848_cast_fp16 = add(x = var_846_cast_fp16, y = var_847_to_fp16)[name = string("op_848_cast_fp16")]; - fp32 var_849_epsilon_0 = const()[name = string("op_849_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_849_cast_fp16 = rsqrt(epsilon = var_849_epsilon_0, x = var_848_cast_fp16)[name = string("op_849_cast_fp16")]; - tensor var_850_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_849_cast_fp16)[name = string("op_850_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(27557184)))]; - tensor var_851_cast_fp16 = mul(x = var_850_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_851_cast_fp16")]; - tensor var_854_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_854_cast_fp16")]; - tensor var_856_axes_0 = const()[name = string("op_856_axes_0"), val = tensor([-1])]; - bool var_856_keep_dims_0 = const()[name = string("op_856_keep_dims_0"), val = bool(true)]; - tensor var_856_cast_fp16 = reduce_mean(axes = var_856_axes_0, keep_dims = var_856_keep_dims_0, x = var_854_cast_fp16)[name = string("op_856_cast_fp16")]; - fp16 var_857_to_fp16 = const()[name = string("op_857_to_fp16"), val = fp16(0x1p-23)]; - tensor var_858_cast_fp16 = add(x = var_856_cast_fp16, y = var_857_to_fp16)[name = string("op_858_cast_fp16")]; - fp32 var_859_epsilon_0 = const()[name = string("op_859_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_859_cast_fp16 = rsqrt(epsilon = var_859_epsilon_0, x = var_858_cast_fp16)[name = string("op_859_cast_fp16")]; - tensor var_860_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_859_cast_fp16)[name = string("op_860_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(27557376)))]; - tensor var_861_cast_fp16 = mul(x = var_860_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_861_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_861_cast_fp16)[name = string("transpose_135")]; - tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_851_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_872_perm_0 = const()[name = string("op_872_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_873 = const()[name = string("op_873"), val = tensor([1, 864, 512])]; - tensor var_872_cast_fp16 = transpose(perm = var_872_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; - tensor input_143_cast_fp16 = reshape(shape = var_873, x = var_872_cast_fp16)[name = string("input_143_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(27557568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27819776))))[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(27820864)))]; - tensor linear_37_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_143_cast_fp16)[name = string("linear_37_cast_fp16")]; - tensor var_879_cast_fp16 = mul(x = var_818_cast_fp16, y = linear_37_cast_fp16)[name = string("op_879_cast_fp16")]; - tensor input_147_cast_fp16 = add(x = input_141_cast_fp16, y = var_879_cast_fp16)[name = string("input_147_cast_fp16")]; - tensor var_881_axes_0 = const()[name = string("op_881_axes_0"), val = tensor([1])]; - tensor var_881_cast_fp16 = expand_dims(axes = var_881_axes_0, x = var_812_cast_fp16_5)[name = string("op_881_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_69_to_fp16, x = input_147_cast_fp16)[name = string("x_37_cast_fp16")]; - tensor var_884_axes_0 = const()[name = string("op_884_axes_0"), val = tensor([1])]; - tensor var_884_cast_fp16 = expand_dims(axes = var_884_axes_0, x = var_812_cast_fp16_4)[name = string("op_884_cast_fp16")]; - fp16 var_885_to_fp16 = const()[name = string("op_885_to_fp16"), val = fp16(0x1p+0)]; - tensor var_886_cast_fp16 = add(x = var_884_cast_fp16, y = var_885_to_fp16)[name = string("op_886_cast_fp16")]; - tensor var_887_cast_fp16 = mul(x = x_37_cast_fp16, y = var_886_cast_fp16)[name = string("op_887_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_812_cast_fp16_3)[name = string("op_888_cast_fp16")]; - tensor input_149_cast_fp16 = add(x = var_887_cast_fp16, y = var_888_cast_fp16)[name = string("input_149_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(27821952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28870592))))[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(28874752)))]; - tensor linear_38_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_149_cast_fp16)[name = string("linear_38_cast_fp16")]; - string input_153_mode_0 = const()[name = string("input_153_mode_0"), val = string("EXACT")]; - tensor input_153_cast_fp16 = gelu(mode = input_153_mode_0, x = linear_38_cast_fp16)[name = string("input_153_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(28878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29927552))))[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(29928640)))]; - tensor linear_39_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_153_cast_fp16)[name = string("linear_39_cast_fp16")]; - tensor var_901_cast_fp16 = mul(x = var_881_cast_fp16, y = linear_39_cast_fp16)[name = string("op_901_cast_fp16")]; - tensor input_161_cast_fp16 = add(x = input_147_cast_fp16, y = var_901_cast_fp16)[name = string("input_161_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(29929728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31502656))))[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(31508864)))]; - tensor linear_40_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_39_cast_fp16)[name = string("linear_40_cast_fp16")]; - tensor var_910_split_sizes_0 = const()[name = string("op_910_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_910_axis_0 = const()[name = string("op_910_axis_0"), val = int32(-1)]; - tensor var_910_cast_fp16_0, tensor var_910_cast_fp16_1, tensor var_910_cast_fp16_2, tensor var_910_cast_fp16_3, tensor var_910_cast_fp16_4, tensor var_910_cast_fp16_5 = split(axis = var_910_axis_0, split_sizes = var_910_split_sizes_0, x = linear_40_cast_fp16)[name = string("op_910_cast_fp16")]; - tensor var_916_axes_0 = const()[name = string("op_916_axes_0"), val = tensor([1])]; - tensor var_916_cast_fp16 = expand_dims(axes = var_916_axes_0, x = var_910_cast_fp16_2)[name = string("op_916_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_69_to_fp16, x = input_161_cast_fp16)[name = string("x_39_cast_fp16")]; - tensor var_919_axes_0 = const()[name = string("op_919_axes_0"), val = tensor([1])]; - tensor var_919_cast_fp16 = expand_dims(axes = var_919_axes_0, x = var_910_cast_fp16_1)[name = string("op_919_cast_fp16")]; - fp16 var_920_to_fp16 = const()[name = string("op_920_to_fp16"), val = fp16(0x1p+0)]; - tensor var_921_cast_fp16 = add(x = var_919_cast_fp16, y = var_920_to_fp16)[name = string("op_921_cast_fp16")]; - tensor var_922_cast_fp16 = mul(x = x_39_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_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_910_cast_fp16_0)[name = string("op_923_cast_fp16")]; - tensor x_41_cast_fp16 = add(x = var_922_cast_fp16, y = var_923_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(31515072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32301568))))[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(32304704)))]; - tensor linear_41_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_41_cast_fp16")]; - tensor var_935 = const()[name = string("op_935"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_13_cast_fp16 = reshape(shape = var_935, x = linear_41_cast_fp16)[name = string("qkv_13_cast_fp16")]; - tensor var_937_split_sizes_0 = const()[name = string("op_937_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_937_axis_0 = const()[name = string("op_937_axis_0"), val = int32(2)]; - tensor var_937_cast_fp16_0, tensor var_937_cast_fp16_1, tensor var_937_cast_fp16_2 = split(axis = var_937_axis_0, split_sizes = var_937_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_937_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_937_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_937_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_937_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; - tensor var_942_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_942_cast_fp16")]; - tensor var_944_axes_0 = const()[name = string("op_944_axes_0"), val = tensor([-1])]; - bool var_944_keep_dims_0 = const()[name = string("op_944_keep_dims_0"), val = bool(true)]; - tensor var_944_cast_fp16 = reduce_mean(axes = var_944_axes_0, keep_dims = var_944_keep_dims_0, x = var_942_cast_fp16)[name = string("op_944_cast_fp16")]; - fp16 var_945_to_fp16 = const()[name = string("op_945_to_fp16"), val = fp16(0x1p-23)]; - tensor var_946_cast_fp16 = add(x = var_944_cast_fp16, y = var_945_to_fp16)[name = string("op_946_cast_fp16")]; - fp32 var_947_epsilon_0 = const()[name = string("op_947_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_947_cast_fp16 = rsqrt(epsilon = var_947_epsilon_0, x = var_946_cast_fp16)[name = string("op_947_cast_fp16")]; - tensor var_948_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_947_cast_fp16)[name = string("op_948_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(32307840)))]; - tensor var_949_cast_fp16 = mul(x = var_948_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_949_cast_fp16")]; - tensor var_952_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_952_cast_fp16")]; - tensor var_954_axes_0 = const()[name = string("op_954_axes_0"), val = tensor([-1])]; - bool var_954_keep_dims_0 = const()[name = string("op_954_keep_dims_0"), val = bool(true)]; - tensor var_954_cast_fp16 = reduce_mean(axes = var_954_axes_0, keep_dims = var_954_keep_dims_0, x = var_952_cast_fp16)[name = string("op_954_cast_fp16")]; - fp16 var_955_to_fp16 = const()[name = string("op_955_to_fp16"), val = fp16(0x1p-23)]; - tensor var_956_cast_fp16 = add(x = var_954_cast_fp16, y = var_955_to_fp16)[name = string("op_956_cast_fp16")]; - fp32 var_957_epsilon_0 = const()[name = string("op_957_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_957_cast_fp16 = rsqrt(epsilon = var_957_epsilon_0, x = var_956_cast_fp16)[name = string("op_957_cast_fp16")]; - tensor var_958_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_957_cast_fp16)[name = string("op_958_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(32308032)))]; - tensor var_959_cast_fp16 = mul(x = var_958_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_959_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_959_cast_fp16)[name = string("transpose_131")]; - tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_949_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_970_perm_0 = const()[name = string("op_970_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_971 = const()[name = string("op_971"), val = tensor([1, 864, 512])]; - tensor var_970_cast_fp16 = transpose(perm = var_970_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; - tensor input_163_cast_fp16 = reshape(shape = var_971, x = var_970_cast_fp16)[name = string("input_163_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(32308224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32570432))))[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(32571520)))]; - tensor linear_42_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_163_cast_fp16)[name = string("linear_42_cast_fp16")]; - tensor var_977_cast_fp16 = mul(x = var_916_cast_fp16, y = linear_42_cast_fp16)[name = string("op_977_cast_fp16")]; - tensor input_167_cast_fp16 = add(x = input_161_cast_fp16, y = var_977_cast_fp16)[name = string("input_167_cast_fp16")]; - tensor var_979_axes_0 = const()[name = string("op_979_axes_0"), val = tensor([1])]; - tensor var_979_cast_fp16 = expand_dims(axes = var_979_axes_0, x = var_910_cast_fp16_5)[name = string("op_979_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_69_to_fp16, x = input_167_cast_fp16)[name = string("x_43_cast_fp16")]; - tensor var_982_axes_0 = const()[name = string("op_982_axes_0"), val = tensor([1])]; - tensor var_982_cast_fp16 = expand_dims(axes = var_982_axes_0, x = var_910_cast_fp16_4)[name = string("op_982_cast_fp16")]; - fp16 var_983_to_fp16 = const()[name = string("op_983_to_fp16"), val = fp16(0x1p+0)]; - tensor var_984_cast_fp16 = add(x = var_982_cast_fp16, y = var_983_to_fp16)[name = string("op_984_cast_fp16")]; - tensor var_985_cast_fp16 = mul(x = x_43_cast_fp16, y = var_984_cast_fp16)[name = string("op_985_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_910_cast_fp16_3)[name = string("op_986_cast_fp16")]; - tensor input_169_cast_fp16 = add(x = var_985_cast_fp16, y = var_986_cast_fp16)[name = string("input_169_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(32572608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33621248))))[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(33625408)))]; - tensor linear_43_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_169_cast_fp16)[name = string("linear_43_cast_fp16")]; - string input_173_mode_0 = const()[name = string("input_173_mode_0"), val = string("EXACT")]; - tensor input_173_cast_fp16 = gelu(mode = input_173_mode_0, x = linear_43_cast_fp16)[name = string("input_173_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(33629568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34678208))))[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(34679296)))]; - tensor linear_44_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_173_cast_fp16)[name = string("linear_44_cast_fp16")]; - tensor var_999_cast_fp16 = mul(x = var_979_cast_fp16, y = linear_44_cast_fp16)[name = string("op_999_cast_fp16")]; - tensor input_181_cast_fp16 = add(x = input_167_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_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(34680384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36253312))))[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(36259520)))]; - tensor linear_45_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_39_cast_fp16)[name = string("linear_45_cast_fp16")]; - tensor var_1008_split_sizes_0 = const()[name = string("op_1008_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1008_axis_0 = const()[name = string("op_1008_axis_0"), val = int32(-1)]; - tensor var_1008_cast_fp16_0, tensor var_1008_cast_fp16_1, tensor var_1008_cast_fp16_2, tensor var_1008_cast_fp16_3, tensor var_1008_cast_fp16_4, tensor var_1008_cast_fp16_5 = split(axis = var_1008_axis_0, split_sizes = var_1008_split_sizes_0, x = linear_45_cast_fp16)[name = string("op_1008_cast_fp16")]; - tensor var_1014_axes_0 = const()[name = string("op_1014_axes_0"), val = tensor([1])]; - tensor var_1014_cast_fp16 = expand_dims(axes = var_1014_axes_0, x = var_1008_cast_fp16_2)[name = string("op_1014_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_69_to_fp16, x = input_181_cast_fp16)[name = string("x_45_cast_fp16")]; - tensor var_1017_axes_0 = const()[name = string("op_1017_axes_0"), val = tensor([1])]; - tensor var_1017_cast_fp16 = expand_dims(axes = var_1017_axes_0, x = var_1008_cast_fp16_1)[name = string("op_1017_cast_fp16")]; - fp16 var_1018_to_fp16 = const()[name = string("op_1018_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1019_cast_fp16 = add(x = var_1017_cast_fp16, y = var_1018_to_fp16)[name = string("op_1019_cast_fp16")]; - tensor var_1020_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_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_1008_cast_fp16_0)[name = string("op_1021_cast_fp16")]; - tensor x_47_cast_fp16 = add(x = var_1020_cast_fp16, y = var_1021_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(36265728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37052224))))[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(37055360)))]; - tensor linear_46_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_46_cast_fp16")]; - tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_15_cast_fp16 = reshape(shape = var_1033, x = linear_46_cast_fp16)[name = string("qkv_15_cast_fp16")]; - tensor var_1035_split_sizes_0 = const()[name = string("op_1035_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1035_axis_0 = const()[name = string("op_1035_axis_0"), val = int32(2)]; - tensor var_1035_cast_fp16_0, tensor var_1035_cast_fp16_1, tensor var_1035_cast_fp16_2 = split(axis = var_1035_axis_0, split_sizes = var_1035_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1035_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_1035_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_1035_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_1035_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; - tensor var_1040_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1040_cast_fp16")]; - tensor var_1042_axes_0 = const()[name = string("op_1042_axes_0"), val = tensor([-1])]; - bool var_1042_keep_dims_0 = const()[name = string("op_1042_keep_dims_0"), val = bool(true)]; - tensor var_1042_cast_fp16 = reduce_mean(axes = var_1042_axes_0, keep_dims = var_1042_keep_dims_0, x = var_1040_cast_fp16)[name = string("op_1042_cast_fp16")]; - fp16 var_1043_to_fp16 = const()[name = string("op_1043_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1044_cast_fp16 = add(x = var_1042_cast_fp16, y = var_1043_to_fp16)[name = string("op_1044_cast_fp16")]; - fp32 var_1045_epsilon_0 = const()[name = string("op_1045_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1045_cast_fp16 = rsqrt(epsilon = var_1045_epsilon_0, x = var_1044_cast_fp16)[name = string("op_1045_cast_fp16")]; - tensor var_1046_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1045_cast_fp16)[name = string("op_1046_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(37058496)))]; - tensor var_1047_cast_fp16 = mul(x = var_1046_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1047_cast_fp16")]; - tensor var_1050_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1050_cast_fp16")]; - tensor var_1052_axes_0 = const()[name = string("op_1052_axes_0"), val = tensor([-1])]; - bool var_1052_keep_dims_0 = const()[name = string("op_1052_keep_dims_0"), val = bool(true)]; - tensor var_1052_cast_fp16 = reduce_mean(axes = var_1052_axes_0, keep_dims = var_1052_keep_dims_0, x = var_1050_cast_fp16)[name = string("op_1052_cast_fp16")]; - fp16 var_1053_to_fp16 = const()[name = string("op_1053_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1054_cast_fp16 = add(x = var_1052_cast_fp16, y = var_1053_to_fp16)[name = string("op_1054_cast_fp16")]; - fp32 var_1055_epsilon_0 = const()[name = string("op_1055_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1055_cast_fp16 = rsqrt(epsilon = var_1055_epsilon_0, x = var_1054_cast_fp16)[name = string("op_1055_cast_fp16")]; - tensor var_1056_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1055_cast_fp16)[name = string("op_1056_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(37058688)))]; - tensor var_1057_cast_fp16 = mul(x = var_1056_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1057_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_1057_cast_fp16)[name = string("transpose_127")]; - tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1047_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_1068_perm_0 = const()[name = string("op_1068_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1069 = const()[name = string("op_1069"), val = tensor([1, 864, 512])]; - tensor var_1068_cast_fp16 = transpose(perm = var_1068_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; - tensor input_183_cast_fp16 = reshape(shape = var_1069, x = var_1068_cast_fp16)[name = string("input_183_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(37058880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37321088))))[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(37322176)))]; - tensor linear_47_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_183_cast_fp16)[name = string("linear_47_cast_fp16")]; - tensor var_1075_cast_fp16 = mul(x = var_1014_cast_fp16, y = linear_47_cast_fp16)[name = string("op_1075_cast_fp16")]; - tensor input_187_cast_fp16 = add(x = input_181_cast_fp16, y = var_1075_cast_fp16)[name = string("input_187_cast_fp16")]; - tensor var_1077_axes_0 = const()[name = string("op_1077_axes_0"), val = tensor([1])]; - tensor var_1077_cast_fp16 = expand_dims(axes = var_1077_axes_0, x = var_1008_cast_fp16_5)[name = string("op_1077_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_69_to_fp16, x = input_187_cast_fp16)[name = string("x_49_cast_fp16")]; - tensor var_1080_axes_0 = const()[name = string("op_1080_axes_0"), val = tensor([1])]; - tensor var_1080_cast_fp16 = expand_dims(axes = var_1080_axes_0, x = var_1008_cast_fp16_4)[name = string("op_1080_cast_fp16")]; - fp16 var_1081_to_fp16 = const()[name = string("op_1081_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1082_cast_fp16 = add(x = var_1080_cast_fp16, y = var_1081_to_fp16)[name = string("op_1082_cast_fp16")]; - tensor var_1083_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1082_cast_fp16)[name = string("op_1083_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_1008_cast_fp16_3)[name = string("op_1084_cast_fp16")]; - tensor input_189_cast_fp16 = add(x = var_1083_cast_fp16, y = var_1084_cast_fp16)[name = string("input_189_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(37323264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38371904))))[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(38376064)))]; - tensor linear_48_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_189_cast_fp16)[name = string("linear_48_cast_fp16")]; - string input_193_mode_0 = const()[name = string("input_193_mode_0"), val = string("EXACT")]; - tensor input_193_cast_fp16 = gelu(mode = input_193_mode_0, x = linear_48_cast_fp16)[name = string("input_193_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(38380224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39428864))))[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(39429952)))]; - tensor linear_49_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_193_cast_fp16)[name = string("linear_49_cast_fp16")]; - tensor var_1097_cast_fp16 = mul(x = var_1077_cast_fp16, y = linear_49_cast_fp16)[name = string("op_1097_cast_fp16")]; - tensor input_201_cast_fp16 = add(x = input_187_cast_fp16, y = var_1097_cast_fp16)[name = string("input_201_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(39431040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41003968))))[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(41010176)))]; - tensor linear_50_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_39_cast_fp16)[name = string("linear_50_cast_fp16")]; - tensor var_1106_split_sizes_0 = const()[name = string("op_1106_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1106_axis_0 = const()[name = string("op_1106_axis_0"), val = int32(-1)]; - tensor var_1106_cast_fp16_0, tensor var_1106_cast_fp16_1, tensor var_1106_cast_fp16_2, tensor var_1106_cast_fp16_3, tensor var_1106_cast_fp16_4, tensor var_1106_cast_fp16_5 = split(axis = var_1106_axis_0, split_sizes = var_1106_split_sizes_0, x = linear_50_cast_fp16)[name = string("op_1106_cast_fp16")]; - tensor var_1112_axes_0 = const()[name = string("op_1112_axes_0"), val = tensor([1])]; - tensor var_1112_cast_fp16 = expand_dims(axes = var_1112_axes_0, x = var_1106_cast_fp16_2)[name = string("op_1112_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_69_to_fp16, x = input_201_cast_fp16)[name = string("x_51_cast_fp16")]; - tensor var_1115_axes_0 = const()[name = string("op_1115_axes_0"), val = tensor([1])]; - tensor var_1115_cast_fp16 = expand_dims(axes = var_1115_axes_0, x = var_1106_cast_fp16_1)[name = string("op_1115_cast_fp16")]; - fp16 var_1116_to_fp16 = const()[name = string("op_1116_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1117_cast_fp16 = add(x = var_1115_cast_fp16, y = var_1116_to_fp16)[name = string("op_1117_cast_fp16")]; - tensor var_1118_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1117_cast_fp16)[name = string("op_1118_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_1106_cast_fp16_0)[name = string("op_1119_cast_fp16")]; - tensor x_53_cast_fp16 = add(x = var_1118_cast_fp16, y = var_1119_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(41016384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41802880))))[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(41806016)))]; - tensor linear_51_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_51_cast_fp16")]; - tensor var_1131 = const()[name = string("op_1131"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_17_cast_fp16 = reshape(shape = var_1131, x = linear_51_cast_fp16)[name = string("qkv_17_cast_fp16")]; - tensor var_1133_split_sizes_0 = const()[name = string("op_1133_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1133_axis_0 = const()[name = string("op_1133_axis_0"), val = int32(2)]; - tensor var_1133_cast_fp16_0, tensor var_1133_cast_fp16_1, tensor var_1133_cast_fp16_2 = split(axis = var_1133_axis_0, split_sizes = var_1133_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1133_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_1133_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_1133_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_1133_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; - tensor var_1138_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1138_cast_fp16")]; - tensor var_1140_axes_0 = const()[name = string("op_1140_axes_0"), val = tensor([-1])]; - bool var_1140_keep_dims_0 = const()[name = string("op_1140_keep_dims_0"), val = bool(true)]; - tensor var_1140_cast_fp16 = reduce_mean(axes = var_1140_axes_0, keep_dims = var_1140_keep_dims_0, x = var_1138_cast_fp16)[name = string("op_1140_cast_fp16")]; - fp16 var_1141_to_fp16 = const()[name = string("op_1141_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1142_cast_fp16 = add(x = var_1140_cast_fp16, y = var_1141_to_fp16)[name = string("op_1142_cast_fp16")]; - fp32 var_1143_epsilon_0 = const()[name = string("op_1143_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1143_cast_fp16 = rsqrt(epsilon = var_1143_epsilon_0, x = var_1142_cast_fp16)[name = string("op_1143_cast_fp16")]; - tensor var_1144_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1143_cast_fp16)[name = string("op_1144_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(41809152)))]; - tensor var_1145_cast_fp16 = mul(x = var_1144_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1145_cast_fp16")]; - tensor var_1148_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1148_cast_fp16")]; - tensor var_1150_axes_0 = const()[name = string("op_1150_axes_0"), val = tensor([-1])]; - bool var_1150_keep_dims_0 = const()[name = string("op_1150_keep_dims_0"), val = bool(true)]; - tensor var_1150_cast_fp16 = reduce_mean(axes = var_1150_axes_0, keep_dims = var_1150_keep_dims_0, x = var_1148_cast_fp16)[name = string("op_1150_cast_fp16")]; - fp16 var_1151_to_fp16 = const()[name = string("op_1151_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1152_cast_fp16 = add(x = var_1150_cast_fp16, y = var_1151_to_fp16)[name = string("op_1152_cast_fp16")]; - fp32 var_1153_epsilon_0 = const()[name = string("op_1153_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1153_cast_fp16 = rsqrt(epsilon = var_1153_epsilon_0, x = var_1152_cast_fp16)[name = string("op_1153_cast_fp16")]; - tensor var_1154_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1153_cast_fp16)[name = string("op_1154_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(41809344)))]; - tensor var_1155_cast_fp16 = mul(x = var_1154_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1155_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_1155_cast_fp16)[name = string("transpose_123")]; - tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1145_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_1166_perm_0 = const()[name = string("op_1166_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1167 = const()[name = string("op_1167"), val = tensor([1, 864, 512])]; - tensor var_1166_cast_fp16 = transpose(perm = var_1166_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; - tensor input_203_cast_fp16 = reshape(shape = var_1167, x = var_1166_cast_fp16)[name = string("input_203_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(41809536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42071744))))[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(42072832)))]; - tensor linear_52_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_203_cast_fp16)[name = string("linear_52_cast_fp16")]; - tensor var_1173_cast_fp16 = mul(x = var_1112_cast_fp16, y = linear_52_cast_fp16)[name = string("op_1173_cast_fp16")]; - tensor input_207_cast_fp16 = add(x = input_201_cast_fp16, y = var_1173_cast_fp16)[name = string("input_207_cast_fp16")]; - tensor var_1175_axes_0 = const()[name = string("op_1175_axes_0"), val = tensor([1])]; - tensor var_1175_cast_fp16 = expand_dims(axes = var_1175_axes_0, x = var_1106_cast_fp16_5)[name = string("op_1175_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_69_to_fp16, x = input_207_cast_fp16)[name = string("x_55_cast_fp16")]; - tensor var_1178_axes_0 = const()[name = string("op_1178_axes_0"), val = tensor([1])]; - tensor var_1178_cast_fp16 = expand_dims(axes = var_1178_axes_0, x = var_1106_cast_fp16_4)[name = string("op_1178_cast_fp16")]; - fp16 var_1179_to_fp16 = const()[name = string("op_1179_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1180_cast_fp16 = add(x = var_1178_cast_fp16, y = var_1179_to_fp16)[name = string("op_1180_cast_fp16")]; - tensor var_1181_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1180_cast_fp16)[name = string("op_1181_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_1106_cast_fp16_3)[name = string("op_1182_cast_fp16")]; - tensor input_209_cast_fp16 = add(x = var_1181_cast_fp16, y = var_1182_cast_fp16)[name = string("input_209_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(42073920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43122560))))[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(43126720)))]; - tensor linear_53_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_209_cast_fp16)[name = string("linear_53_cast_fp16")]; - string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("EXACT")]; - tensor input_213_cast_fp16 = gelu(mode = input_213_mode_0, x = linear_53_cast_fp16)[name = string("input_213_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(43130880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44179520))))[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(44180608)))]; - tensor linear_54_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_213_cast_fp16)[name = string("linear_54_cast_fp16")]; - tensor var_1195_cast_fp16 = mul(x = var_1175_cast_fp16, y = linear_54_cast_fp16)[name = string("op_1195_cast_fp16")]; - tensor input_221_cast_fp16 = add(x = input_207_cast_fp16, y = var_1195_cast_fp16)[name = string("input_221_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(44181696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45754624))))[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(45760832)))]; - tensor linear_55_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_39_cast_fp16)[name = string("linear_55_cast_fp16")]; - tensor var_1204_split_sizes_0 = const()[name = string("op_1204_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1204_axis_0 = const()[name = string("op_1204_axis_0"), val = int32(-1)]; - tensor var_1204_cast_fp16_0, tensor var_1204_cast_fp16_1, tensor var_1204_cast_fp16_2, tensor var_1204_cast_fp16_3, tensor var_1204_cast_fp16_4, tensor var_1204_cast_fp16_5 = split(axis = var_1204_axis_0, split_sizes = var_1204_split_sizes_0, x = linear_55_cast_fp16)[name = string("op_1204_cast_fp16")]; - tensor var_1210_axes_0 = const()[name = string("op_1210_axes_0"), val = tensor([1])]; - tensor var_1210_cast_fp16 = expand_dims(axes = var_1210_axes_0, x = var_1204_cast_fp16_2)[name = string("op_1210_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_69_to_fp16, x = input_221_cast_fp16)[name = string("x_57_cast_fp16")]; - tensor var_1213_axes_0 = const()[name = string("op_1213_axes_0"), val = tensor([1])]; - tensor var_1213_cast_fp16 = expand_dims(axes = var_1213_axes_0, x = var_1204_cast_fp16_1)[name = string("op_1213_cast_fp16")]; - fp16 var_1214_to_fp16 = const()[name = string("op_1214_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1215_cast_fp16 = add(x = var_1213_cast_fp16, y = var_1214_to_fp16)[name = string("op_1215_cast_fp16")]; - tensor var_1216_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1215_cast_fp16)[name = string("op_1216_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_1204_cast_fp16_0)[name = string("op_1217_cast_fp16")]; - tensor x_59_cast_fp16 = add(x = var_1216_cast_fp16, y = var_1217_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(45767040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46553536))))[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(46556672)))]; - tensor linear_56_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_56_cast_fp16")]; - tensor var_1229 = const()[name = string("op_1229"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_19_cast_fp16 = reshape(shape = var_1229, x = linear_56_cast_fp16)[name = string("qkv_19_cast_fp16")]; - tensor var_1231_split_sizes_0 = const()[name = string("op_1231_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1231_axis_0 = const()[name = string("op_1231_axis_0"), val = int32(2)]; - tensor var_1231_cast_fp16_0, tensor var_1231_cast_fp16_1, tensor var_1231_cast_fp16_2 = split(axis = var_1231_axis_0, split_sizes = var_1231_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1231_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_1231_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_1231_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_1231_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; - tensor var_1236_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1236_cast_fp16")]; - tensor var_1238_axes_0 = const()[name = string("op_1238_axes_0"), val = tensor([-1])]; - bool var_1238_keep_dims_0 = const()[name = string("op_1238_keep_dims_0"), val = bool(true)]; - tensor var_1238_cast_fp16 = reduce_mean(axes = var_1238_axes_0, keep_dims = var_1238_keep_dims_0, x = var_1236_cast_fp16)[name = string("op_1238_cast_fp16")]; - fp16 var_1239_to_fp16 = const()[name = string("op_1239_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1240_cast_fp16 = add(x = var_1238_cast_fp16, y = var_1239_to_fp16)[name = string("op_1240_cast_fp16")]; - fp32 var_1241_epsilon_0 = const()[name = string("op_1241_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1241_cast_fp16 = rsqrt(epsilon = var_1241_epsilon_0, x = var_1240_cast_fp16)[name = string("op_1241_cast_fp16")]; - tensor var_1242_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1241_cast_fp16)[name = string("op_1242_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(46559808)))]; - tensor var_1243_cast_fp16 = mul(x = var_1242_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1243_cast_fp16")]; - tensor var_1246_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1246_cast_fp16")]; - tensor var_1248_axes_0 = const()[name = string("op_1248_axes_0"), val = tensor([-1])]; - bool var_1248_keep_dims_0 = const()[name = string("op_1248_keep_dims_0"), val = bool(true)]; - tensor var_1248_cast_fp16 = reduce_mean(axes = var_1248_axes_0, keep_dims = var_1248_keep_dims_0, x = var_1246_cast_fp16)[name = string("op_1248_cast_fp16")]; - fp16 var_1249_to_fp16 = const()[name = string("op_1249_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1250_cast_fp16 = add(x = var_1248_cast_fp16, y = var_1249_to_fp16)[name = string("op_1250_cast_fp16")]; - fp32 var_1251_epsilon_0 = const()[name = string("op_1251_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1251_cast_fp16 = rsqrt(epsilon = var_1251_epsilon_0, x = var_1250_cast_fp16)[name = string("op_1251_cast_fp16")]; - tensor var_1252_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1251_cast_fp16)[name = string("op_1252_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(46560000)))]; - tensor var_1253_cast_fp16 = mul(x = var_1252_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1253_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_1253_cast_fp16)[name = string("transpose_119")]; - tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1243_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_1264_perm_0 = const()[name = string("op_1264_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1265 = const()[name = string("op_1265"), val = tensor([1, 864, 512])]; - tensor var_1264_cast_fp16 = transpose(perm = var_1264_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; - tensor input_223_cast_fp16 = reshape(shape = var_1265, x = var_1264_cast_fp16)[name = string("input_223_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(46560192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46822400))))[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(46823488)))]; - tensor linear_57_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_223_cast_fp16)[name = string("linear_57_cast_fp16")]; - tensor var_1271_cast_fp16 = mul(x = var_1210_cast_fp16, y = linear_57_cast_fp16)[name = string("op_1271_cast_fp16")]; - tensor input_227_cast_fp16 = add(x = input_221_cast_fp16, y = var_1271_cast_fp16)[name = string("input_227_cast_fp16")]; - tensor var_1273_axes_0 = const()[name = string("op_1273_axes_0"), val = tensor([1])]; - tensor var_1273_cast_fp16 = expand_dims(axes = var_1273_axes_0, x = var_1204_cast_fp16_5)[name = string("op_1273_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_69_to_fp16, x = input_227_cast_fp16)[name = string("x_61_cast_fp16")]; - tensor var_1276_axes_0 = const()[name = string("op_1276_axes_0"), val = tensor([1])]; - tensor var_1276_cast_fp16 = expand_dims(axes = var_1276_axes_0, x = var_1204_cast_fp16_4)[name = string("op_1276_cast_fp16")]; - fp16 var_1277_to_fp16 = const()[name = string("op_1277_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1278_cast_fp16 = add(x = var_1276_cast_fp16, y = var_1277_to_fp16)[name = string("op_1278_cast_fp16")]; - tensor var_1279_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1278_cast_fp16)[name = string("op_1279_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_1204_cast_fp16_3)[name = string("op_1280_cast_fp16")]; - tensor input_229_cast_fp16 = add(x = var_1279_cast_fp16, y = var_1280_cast_fp16)[name = string("input_229_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(46824576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47873216))))[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(47877376)))]; - tensor linear_58_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_229_cast_fp16)[name = string("linear_58_cast_fp16")]; - string input_233_mode_0 = const()[name = string("input_233_mode_0"), val = string("EXACT")]; - tensor input_233_cast_fp16 = gelu(mode = input_233_mode_0, x = linear_58_cast_fp16)[name = string("input_233_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(47881536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48930176))))[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(48931264)))]; - tensor linear_59_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_233_cast_fp16)[name = string("linear_59_cast_fp16")]; - tensor var_1293_cast_fp16 = mul(x = var_1273_cast_fp16, y = linear_59_cast_fp16)[name = string("op_1293_cast_fp16")]; - tensor input_241_cast_fp16 = add(x = input_227_cast_fp16, y = var_1293_cast_fp16)[name = string("input_241_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(48932352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50505280))))[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(50511488)))]; - tensor linear_60_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_39_cast_fp16)[name = string("linear_60_cast_fp16")]; - tensor var_1302_split_sizes_0 = const()[name = string("op_1302_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1302_axis_0 = const()[name = string("op_1302_axis_0"), val = int32(-1)]; - tensor var_1302_cast_fp16_0, tensor var_1302_cast_fp16_1, tensor var_1302_cast_fp16_2, tensor var_1302_cast_fp16_3, tensor var_1302_cast_fp16_4, tensor var_1302_cast_fp16_5 = split(axis = var_1302_axis_0, split_sizes = var_1302_split_sizes_0, x = linear_60_cast_fp16)[name = string("op_1302_cast_fp16")]; - tensor var_1308_axes_0 = const()[name = string("op_1308_axes_0"), val = tensor([1])]; - tensor var_1308_cast_fp16 = expand_dims(axes = var_1308_axes_0, x = var_1302_cast_fp16_2)[name = string("op_1308_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_69_to_fp16, x = input_241_cast_fp16)[name = string("x_63_cast_fp16")]; - tensor var_1311_axes_0 = const()[name = string("op_1311_axes_0"), val = tensor([1])]; - tensor var_1311_cast_fp16 = expand_dims(axes = var_1311_axes_0, x = var_1302_cast_fp16_1)[name = string("op_1311_cast_fp16")]; - fp16 var_1312_to_fp16 = const()[name = string("op_1312_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1313_cast_fp16 = add(x = var_1311_cast_fp16, y = var_1312_to_fp16)[name = string("op_1313_cast_fp16")]; - tensor var_1314_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1313_cast_fp16)[name = string("op_1314_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_1302_cast_fp16_0)[name = string("op_1315_cast_fp16")]; - tensor x_65_cast_fp16 = add(x = var_1314_cast_fp16, y = var_1315_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(50517696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51304192))))[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(51307328)))]; - tensor linear_61_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_61_cast_fp16")]; - tensor var_1327 = const()[name = string("op_1327"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_21_cast_fp16 = reshape(shape = var_1327, x = linear_61_cast_fp16)[name = string("qkv_21_cast_fp16")]; - tensor var_1329_split_sizes_0 = const()[name = string("op_1329_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1329_axis_0 = const()[name = string("op_1329_axis_0"), val = int32(2)]; - tensor var_1329_cast_fp16_0, tensor var_1329_cast_fp16_1, tensor var_1329_cast_fp16_2 = split(axis = var_1329_axis_0, split_sizes = var_1329_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1329_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_1329_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_1329_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_1329_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; - tensor var_1334_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1334_cast_fp16")]; - tensor var_1336_axes_0 = const()[name = string("op_1336_axes_0"), val = tensor([-1])]; - bool var_1336_keep_dims_0 = const()[name = string("op_1336_keep_dims_0"), val = bool(true)]; - tensor var_1336_cast_fp16 = reduce_mean(axes = var_1336_axes_0, keep_dims = var_1336_keep_dims_0, x = var_1334_cast_fp16)[name = string("op_1336_cast_fp16")]; - fp16 var_1337_to_fp16 = const()[name = string("op_1337_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1338_cast_fp16 = add(x = var_1336_cast_fp16, y = var_1337_to_fp16)[name = string("op_1338_cast_fp16")]; - fp32 var_1339_epsilon_0 = const()[name = string("op_1339_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1339_cast_fp16 = rsqrt(epsilon = var_1339_epsilon_0, x = var_1338_cast_fp16)[name = string("op_1339_cast_fp16")]; - tensor var_1340_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1339_cast_fp16)[name = string("op_1340_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(51310464)))]; - tensor var_1341_cast_fp16 = mul(x = var_1340_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1341_cast_fp16")]; - tensor var_1344_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1344_cast_fp16")]; - tensor var_1346_axes_0 = const()[name = string("op_1346_axes_0"), val = tensor([-1])]; - bool var_1346_keep_dims_0 = const()[name = string("op_1346_keep_dims_0"), val = bool(true)]; - tensor var_1346_cast_fp16 = reduce_mean(axes = var_1346_axes_0, keep_dims = var_1346_keep_dims_0, x = var_1344_cast_fp16)[name = string("op_1346_cast_fp16")]; - fp16 var_1347_to_fp16 = const()[name = string("op_1347_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1348_cast_fp16 = add(x = var_1346_cast_fp16, y = var_1347_to_fp16)[name = string("op_1348_cast_fp16")]; - fp32 var_1349_epsilon_0 = const()[name = string("op_1349_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1349_cast_fp16 = rsqrt(epsilon = var_1349_epsilon_0, x = var_1348_cast_fp16)[name = string("op_1349_cast_fp16")]; - tensor var_1350_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1349_cast_fp16)[name = string("op_1350_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(51310656)))]; - tensor var_1351_cast_fp16 = mul(x = var_1350_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1351_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_1351_cast_fp16)[name = string("transpose_115")]; - tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1341_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_1362_perm_0 = const()[name = string("op_1362_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1363 = const()[name = string("op_1363"), val = tensor([1, 864, 512])]; - tensor var_1362_cast_fp16 = transpose(perm = var_1362_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; - tensor input_243_cast_fp16 = reshape(shape = var_1363, x = var_1362_cast_fp16)[name = string("input_243_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(51310848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51573056))))[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(51574144)))]; - tensor linear_62_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_243_cast_fp16)[name = string("linear_62_cast_fp16")]; - tensor var_1369_cast_fp16 = mul(x = var_1308_cast_fp16, y = linear_62_cast_fp16)[name = string("op_1369_cast_fp16")]; - tensor input_247_cast_fp16 = add(x = input_241_cast_fp16, y = var_1369_cast_fp16)[name = string("input_247_cast_fp16")]; - tensor var_1371_axes_0 = const()[name = string("op_1371_axes_0"), val = tensor([1])]; - tensor var_1371_cast_fp16 = expand_dims(axes = var_1371_axes_0, x = var_1302_cast_fp16_5)[name = string("op_1371_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_69_to_fp16, x = input_247_cast_fp16)[name = string("x_67_cast_fp16")]; - tensor var_1374_axes_0 = const()[name = string("op_1374_axes_0"), val = tensor([1])]; - tensor var_1374_cast_fp16 = expand_dims(axes = var_1374_axes_0, x = var_1302_cast_fp16_4)[name = string("op_1374_cast_fp16")]; - fp16 var_1375_to_fp16 = const()[name = string("op_1375_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1376_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1375_to_fp16)[name = string("op_1376_cast_fp16")]; - tensor var_1377_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1376_cast_fp16)[name = string("op_1377_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_1302_cast_fp16_3)[name = string("op_1378_cast_fp16")]; - tensor input_249_cast_fp16 = add(x = var_1377_cast_fp16, y = var_1378_cast_fp16)[name = string("input_249_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(51575232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52623872))))[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(52628032)))]; - tensor linear_63_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_249_cast_fp16)[name = string("linear_63_cast_fp16")]; - string input_253_mode_0 = const()[name = string("input_253_mode_0"), val = string("EXACT")]; - tensor input_253_cast_fp16 = gelu(mode = input_253_mode_0, x = linear_63_cast_fp16)[name = string("input_253_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(52632192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53680832))))[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(53681920)))]; - tensor linear_64_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_253_cast_fp16)[name = string("linear_64_cast_fp16")]; - tensor var_1391_cast_fp16 = mul(x = var_1371_cast_fp16, y = linear_64_cast_fp16)[name = string("op_1391_cast_fp16")]; - tensor input_261_cast_fp16 = add(x = input_247_cast_fp16, y = var_1391_cast_fp16)[name = string("input_261_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(53683008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55255936))))[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(55262144)))]; - tensor linear_65_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_39_cast_fp16)[name = string("linear_65_cast_fp16")]; - tensor var_1400_split_sizes_0 = const()[name = string("op_1400_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; - int32 var_1400_axis_0 = const()[name = string("op_1400_axis_0"), val = int32(-1)]; - tensor var_1400_cast_fp16_0, tensor var_1400_cast_fp16_1, tensor var_1400_cast_fp16_2, tensor var_1400_cast_fp16_3, tensor var_1400_cast_fp16_4, tensor var_1400_cast_fp16_5 = split(axis = var_1400_axis_0, split_sizes = var_1400_split_sizes_0, x = linear_65_cast_fp16)[name = string("op_1400_cast_fp16")]; - tensor var_1406_axes_0 = const()[name = string("op_1406_axes_0"), val = tensor([1])]; - tensor var_1406_cast_fp16 = expand_dims(axes = var_1406_axes_0, x = var_1400_cast_fp16_2)[name = string("op_1406_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_69_to_fp16, x = input_261_cast_fp16)[name = string("x_69_cast_fp16")]; - tensor var_1409_axes_0 = const()[name = string("op_1409_axes_0"), val = tensor([1])]; - tensor var_1409_cast_fp16 = expand_dims(axes = var_1409_axes_0, x = var_1400_cast_fp16_1)[name = string("op_1409_cast_fp16")]; - fp16 var_1410_to_fp16 = const()[name = string("op_1410_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1411_cast_fp16 = add(x = var_1409_cast_fp16, y = var_1410_to_fp16)[name = string("op_1411_cast_fp16")]; - tensor var_1412_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1411_cast_fp16)[name = string("op_1412_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_1400_cast_fp16_0)[name = string("op_1413_cast_fp16")]; - tensor x_71_cast_fp16 = add(x = var_1412_cast_fp16, y = var_1413_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(55268352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56054848))))[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(56057984)))]; - tensor linear_66_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_66_cast_fp16")]; - tensor var_1425 = const()[name = string("op_1425"), val = tensor([1, 864, 3, 8, 64])]; - tensor qkv_23_cast_fp16 = reshape(shape = var_1425, x = linear_66_cast_fp16)[name = string("qkv_23_cast_fp16")]; - tensor var_1427_split_sizes_0 = const()[name = string("op_1427_split_sizes_0"), val = tensor([1, 1, 1])]; - int32 var_1427_axis_0 = const()[name = string("op_1427_axis_0"), val = int32(2)]; - tensor var_1427_cast_fp16_0, tensor var_1427_cast_fp16_1, tensor var_1427_cast_fp16_2 = split(axis = var_1427_axis_0, split_sizes = var_1427_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1427_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_1427_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_1427_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_1427_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; - tensor var_1432_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1432_cast_fp16")]; - tensor var_1434_axes_0 = const()[name = string("op_1434_axes_0"), val = tensor([-1])]; - bool var_1434_keep_dims_0 = const()[name = string("op_1434_keep_dims_0"), val = bool(true)]; - tensor var_1434_cast_fp16 = reduce_mean(axes = var_1434_axes_0, keep_dims = var_1434_keep_dims_0, x = var_1432_cast_fp16)[name = string("op_1434_cast_fp16")]; - fp16 var_1435_to_fp16 = const()[name = string("op_1435_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1436_cast_fp16 = add(x = var_1434_cast_fp16, y = var_1435_to_fp16)[name = string("op_1436_cast_fp16")]; - fp32 var_1437_epsilon_0 = const()[name = string("op_1437_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1437_cast_fp16 = rsqrt(epsilon = var_1437_epsilon_0, x = var_1436_cast_fp16)[name = string("op_1437_cast_fp16")]; - tensor var_1438_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1437_cast_fp16)[name = string("op_1438_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(56061120)))]; - tensor var_1439_cast_fp16 = mul(x = var_1438_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1439_cast_fp16")]; - tensor var_1442_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1442_cast_fp16")]; - tensor var_1444_axes_0 = const()[name = string("op_1444_axes_0"), val = tensor([-1])]; - bool var_1444_keep_dims_0 = const()[name = string("op_1444_keep_dims_0"), val = bool(true)]; - tensor var_1444_cast_fp16 = reduce_mean(axes = var_1444_axes_0, keep_dims = var_1444_keep_dims_0, x = var_1442_cast_fp16)[name = string("op_1444_cast_fp16")]; - fp16 var_1445_to_fp16 = const()[name = string("op_1445_to_fp16"), val = fp16(0x1p-23)]; - tensor var_1446_cast_fp16 = add(x = var_1444_cast_fp16, y = var_1445_to_fp16)[name = string("op_1446_cast_fp16")]; - fp32 var_1447_epsilon_0 = const()[name = string("op_1447_epsilon_0"), val = fp32(0x1.197998p-40)]; - tensor var_1447_cast_fp16 = rsqrt(epsilon = var_1447_epsilon_0, x = var_1446_cast_fp16)[name = string("op_1447_cast_fp16")]; - tensor var_1448_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1447_cast_fp16)[name = string("op_1448_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(56061312)))]; - tensor var_1449_cast_fp16 = mul(x = var_1448_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1449_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_1449_cast_fp16)[name = string("transpose_111")]; - tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1439_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_1460_perm_0 = const()[name = string("op_1460_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1461 = const()[name = string("op_1461"), val = tensor([1, 864, 512])]; - tensor var_1460_cast_fp16 = transpose(perm = var_1460_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; - tensor input_263_cast_fp16 = reshape(shape = var_1461, x = var_1460_cast_fp16)[name = string("input_263_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(56061504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56323712))))[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(56324800)))]; - tensor linear_67_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_263_cast_fp16)[name = string("linear_67_cast_fp16")]; - tensor var_1467_cast_fp16 = mul(x = var_1406_cast_fp16, y = linear_67_cast_fp16)[name = string("op_1467_cast_fp16")]; - tensor input_267_cast_fp16 = add(x = input_261_cast_fp16, y = var_1467_cast_fp16)[name = string("input_267_cast_fp16")]; - tensor var_1469_axes_0 = const()[name = string("op_1469_axes_0"), val = tensor([1])]; - tensor var_1469_cast_fp16 = expand_dims(axes = var_1469_axes_0, x = var_1400_cast_fp16_5)[name = string("op_1469_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_69_to_fp16, x = input_267_cast_fp16)[name = string("x_73_cast_fp16")]; - tensor var_1472_axes_0 = const()[name = string("op_1472_axes_0"), val = tensor([1])]; - tensor var_1472_cast_fp16 = expand_dims(axes = var_1472_axes_0, x = var_1400_cast_fp16_4)[name = string("op_1472_cast_fp16")]; - fp16 var_1473_to_fp16 = const()[name = string("op_1473_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1474_cast_fp16 = add(x = var_1472_cast_fp16, y = var_1473_to_fp16)[name = string("op_1474_cast_fp16")]; - tensor var_1475_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1474_cast_fp16)[name = string("op_1475_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_1400_cast_fp16_3)[name = string("op_1476_cast_fp16")]; - tensor input_269_cast_fp16 = add(x = var_1475_cast_fp16, y = var_1476_cast_fp16)[name = string("input_269_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(56325888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57374528))))[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(57378688)))]; - tensor linear_68_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_269_cast_fp16)[name = string("linear_68_cast_fp16")]; - string input_273_mode_0 = const()[name = string("input_273_mode_0"), val = string("EXACT")]; - tensor input_273_cast_fp16 = gelu(mode = input_273_mode_0, x = linear_68_cast_fp16)[name = string("input_273_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(57382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58431488))))[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(58432576)))]; - tensor linear_69_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_273_cast_fp16)[name = string("linear_69_cast_fp16")]; - tensor var_1489_cast_fp16 = mul(x = var_1469_cast_fp16, y = linear_69_cast_fp16)[name = string("op_1489_cast_fp16")]; - tensor input_281_cast_fp16 = add(x = input_267_cast_fp16, y = var_1489_cast_fp16)[name = string("input_281_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(58433664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58958016))))[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(58960128)))]; - tensor linear_70_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_39_cast_fp16)[name = string("linear_70_cast_fp16")]; - tensor var_1495_split_sizes_0 = const()[name = string("op_1495_split_sizes_0"), val = tensor([512, 512])]; - int32 var_1495_axis_0 = const()[name = string("op_1495_axis_0"), val = int32(-1)]; - tensor var_1495_cast_fp16_0, tensor var_1495_cast_fp16_1 = split(axis = var_1495_axis_0, split_sizes = var_1495_split_sizes_0, x = linear_70_cast_fp16)[name = string("op_1495_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_69_to_fp16, x = input_281_cast_fp16)[name = string("x_cast_fp16")]; - tensor var_1499_axes_0 = const()[name = string("op_1499_axes_0"), val = tensor([1])]; - tensor var_1499_cast_fp16 = expand_dims(axes = var_1499_axes_0, x = var_1495_cast_fp16_1)[name = string("op_1499_cast_fp16")]; - fp16 var_1500_to_fp16 = const()[name = string("op_1500_to_fp16"), val = fp16(0x1p+0)]; - tensor var_1501_cast_fp16 = add(x = var_1499_cast_fp16, y = var_1500_to_fp16)[name = string("op_1501_cast_fp16")]; - tensor var_1502_cast_fp16 = mul(x = x_cast_fp16, y = var_1501_cast_fp16)[name = string("op_1502_cast_fp16")]; - tensor var_1503_axes_0 = const()[name = string("op_1503_axes_0"), val = tensor([1])]; - tensor var_1503_cast_fp16 = expand_dims(axes = var_1503_axes_0, x = var_1495_cast_fp16_0)[name = string("op_1503_cast_fp16")]; - tensor encoded_cast_fp16 = add(x = var_1502_cast_fp16, y = var_1503_cast_fp16)[name = string("encoded_cast_fp16")]; - tensor input_283_begin_0 = const()[name = string("input_283_begin_0"), val = tensor([0, 96, 0])]; - tensor input_283_end_0 = const()[name = string("input_283_end_0"), val = tensor([1, 864, 512])]; - tensor input_283_end_mask_0 = const()[name = string("input_283_end_mask_0"), val = tensor([true, true, true])]; - tensor input_283_cast_fp16 = slice_by_index(begin = input_283_begin_0, end = input_283_end_0, end_mask = input_283_end_mask_0, x = encoded_cast_fp16)[name = string("input_283_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(58962240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59224448))))[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(59225536)))]; - tensor linear_71_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_71_cast_fp16")]; - string input_287_mode_0 = const()[name = string("input_287_mode_0"), val = string("EXACT")]; - tensor input_287_cast_fp16 = gelu(mode = input_287_mode_0, x = linear_71_cast_fp16)[name = string("input_287_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(59226624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59238976))))[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(59239104)))]; - tensor linear_72_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_72_cast_fp16")]; - tensor var_1517_perm_0 = const()[name = string("op_1517_perm_0"), val = tensor([0, 2, 1])]; - string var_1517_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1517_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; - tensor var_1517_cast_fp16 = transpose(perm = var_1517_perm_0, x = linear_72_cast_fp16)[name = string("transpose_108")]; - tensor velocity = cast(dtype = var_1517_cast_fp16_to_fp32_dtype_0, x = var_1517_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 deleted file mode 100644 index af399fd5d84aae67b7effe444f3cd2d2dee6eef0..0000000000000000000000000000000000000000 --- a/coreml/coreml/vector_estimator.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dfcd7719b9406f6a181f17f48125c33213196f9e722844f88ecbae5a580a8015 -size 60754048 diff --git a/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin deleted file mode 100644 index b86c96d9a45d74d5091c4fad5aabc4ae5b3d1e09..0000000000000000000000000000000000000000 --- a/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1265ad8d6e6ff623afa255fc3eab94c840e6b372ec6d2ebcacfe9ee053c9aade -size 243 diff --git a/coreml/coreml/vocoder.mlmodelc/coremldata.bin b/coreml/coreml/vocoder.mlmodelc/coremldata.bin deleted file mode 100644 index 9c2913a225edcb7b090fb3eb1d0222641e6b3755..0000000000000000000000000000000000000000 --- a/coreml/coreml/vocoder.mlmodelc/coremldata.bin +++ /dev/null @@ -1,3 +0,0 @@ -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 deleted file mode 100644 index 20589cf22cc44d14b3541f1b9ecda663ff9da6ac..0000000000000000000000000000000000000000 --- a/coreml/coreml/vocoder.mlmodelc/model.mil +++ /dev/null @@ -1,5386 +0,0 @@ -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 = const()[name = string("module_adapter_voice_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440)))]; - 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(10)]; - 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(10)]; - 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)[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(37184)))]; - 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(42624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47680))))[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(47808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785152))))[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(785984)))]; - 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(786816)))]; - 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(787648)))]; - 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(788480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[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(936832)))]; - 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(937664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675008))))[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(1675840)))]; - 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(1676672)))]; - 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(1677504)))]; - 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(1678336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825856))))[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(1826688)))]; - 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(1827520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564864))))[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(2565696)))]; - 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(2566528)))]; - 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(2567360)))]; - 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(2568192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715712))))[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(2716544)))]; - 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(2717376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454720))))[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(3455552)))]; - 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(3456384)))]; - 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(3457216)))]; - 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(3458048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605568))))[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(3606400)))]; - 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(3607232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344576))))[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(4345408)))]; - 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(4346240)))]; - 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(4347072)))]; - 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(4347904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4495424))))[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(4496256)))]; - 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(4497088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234432))))[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(5235264)))]; - 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(5236096)))]; - 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(5236928)))]; - 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(5237760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5385280))))[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(5386112)))]; - 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(5386944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124288))))[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(6125120)))]; - 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(6125952)))]; - 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(6126784)))]; - 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(6127616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6275136))))[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(6275968)))]; - 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(6276800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014144))))[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(7014976)))]; - 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(7015808)))]; - 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(7016640)))]; - 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(7017472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164992))))[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(7165824)))]; - 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(7166656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904000))))[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(7904832)))]; - 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(7905664)))]; - 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(7906496)))]; - 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(7907328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054848))))[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(8055680)))]; - 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(8056512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793856))))[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(8794688)))]; - 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(8795520)))]; - 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(8796352)))]; - 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(8797184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944704))))[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(8945536)))]; - 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(8946368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683712))))[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(9684544)))]; - 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(9685376)))]; - 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(9686208)))]; - 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(9687040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834560))))[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(9835392)))]; - 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(9836224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573568))))[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(10574400)))]; - 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(10575232)))]; - 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(10576064)))]; - 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(10576896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10724416))))[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(10725248)))]; - 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(10726080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315968))))[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(11316800)))]; - 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(11317632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054976))))[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(12055808)))]; - 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(12056640)))]; - 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(12057472)))]; - 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(12058304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205824))))[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(12206656)))]; - 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(12207488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944832))))[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(12945664)))]; - 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(12946496)))]; - 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(12947328)))]; - 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(12948160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095680))))[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(13096512)))]; - 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(13097344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13212608))))[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(13212928)))]; - 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(13213248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712))))[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(13572800)))]; - 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(13573888)))]; - 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(13574976)))]; - 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(13576064)))]; - 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(13583296)))]; - 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(13584384)))]; - 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(13585472)))]; - 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(13586560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14373056))))[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(14376192)))]; - 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(14379328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824))))[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(15166912)))]; - 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(15168000)))]; - 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(15175232)))]; - 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(15176320)))]; - 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(15177408)))]; - 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(15178496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15964992))))[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(15968128)))]; - 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(15971264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760))))[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(16758848)))]; - 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(16759936)))]; - 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(16767168)))]; - 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(16768256)))]; - 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(16769344)))]; - 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(16770432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17556928))))[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(17560064)))]; - 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(17563200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696))))[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(18350784)))]; - 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(18351872)))]; - 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(18359104)))]; - 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(18360192)))]; - 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(18361280)))]; - 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(18362368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19148864))))[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(19152000)))]; - 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(19155136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632))))[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(19942720)))]; - 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(19943808)))]; - 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(19951040)))]; - 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(19952128)))]; - 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(19953216)))]; - 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(19954304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20740800))))[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(20743936)))]; - 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(20747072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568))))[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(21534656)))]; - 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(21535744)))]; - 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(21542976)))]; - 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(21544064)))]; - 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(21545152)))]; - 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(21546240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22332736))))[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(22335872)))]; - 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(22339008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504))))[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(23126592)))]; - 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(23127680)))]; - 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(23134912)))]; - 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(23136000)))]; - 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(23137088)))]; - 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(23138176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23924672))))[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(23927808)))]; - 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(23930944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440))))[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(24718528)))]; - 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(24719616)))]; - 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(24726848)))]; - 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(24727936)))]; - 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(24729024)))]; - 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(24730112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25516608))))[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(25519744)))]; - 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(25522880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376))))[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(26310464)))]; - 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(26311552)))]; - 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(26312640)))]; - 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(26313728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104))))[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(26841280)))]; - 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(26843456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368832))))[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(27370944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896320))))[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(27898432)))]; - 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(29995456)))]; - 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 = const()[name = string("module_adapter_voice_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440)))]; - 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(10)]; - 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(10)]; - 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)[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(37184)))]; - 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(42624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47680))))[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(47808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785152))))[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(785984)))]; - 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(786816)))]; - 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(787648)))]; - 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(788480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[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(936832)))]; - 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(937664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675008))))[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(1675840)))]; - 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(1676672)))]; - 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(1677504)))]; - 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(1678336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825856))))[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(1826688)))]; - 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(1827520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564864))))[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(2565696)))]; - 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(2566528)))]; - 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(2567360)))]; - 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(2568192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715712))))[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(2716544)))]; - 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(2717376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454720))))[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(3455552)))]; - 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(3456384)))]; - 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(3457216)))]; - 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(3458048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605568))))[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(3606400)))]; - 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(3607232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344576))))[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(4345408)))]; - 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(4346240)))]; - 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(4347072)))]; - 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(4347904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4495424))))[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(4496256)))]; - 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(4497088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234432))))[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(5235264)))]; - 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(5236096)))]; - 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(5236928)))]; - 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(5237760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5385280))))[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(5386112)))]; - 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(5386944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124288))))[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(6125120)))]; - 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(6125952)))]; - 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(6126784)))]; - 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(6127616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6275136))))[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(6275968)))]; - 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(6276800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014144))))[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(7014976)))]; - 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(7015808)))]; - 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(7016640)))]; - 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(7017472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164992))))[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(7165824)))]; - 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(7166656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904000))))[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(7904832)))]; - 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(7905664)))]; - 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(7906496)))]; - 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(7907328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054848))))[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(8055680)))]; - 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(8056512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793856))))[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(8794688)))]; - 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(8795520)))]; - 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(8796352)))]; - 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(8797184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944704))))[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(8945536)))]; - 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(8946368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683712))))[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(9684544)))]; - 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(9685376)))]; - 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(9686208)))]; - 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(9687040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834560))))[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(9835392)))]; - 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(9836224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573568))))[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(10574400)))]; - 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(10575232)))]; - 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(10576064)))]; - 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(10576896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10724416))))[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(10725248)))]; - 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(10726080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315968))))[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(11316800)))]; - 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(11317632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054976))))[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(12055808)))]; - 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(12056640)))]; - 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(12057472)))]; - 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(12058304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205824))))[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(12206656)))]; - 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(12207488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944832))))[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(12945664)))]; - 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(12946496)))]; - 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(12947328)))]; - 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(12948160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095680))))[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(13096512)))]; - 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(13097344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13212608))))[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(13212928)))]; - 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(13213248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712))))[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(13572800)))]; - 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(13573888)))]; - 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(13574976)))]; - 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(13576064)))]; - 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(13583296)))]; - 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(13584384)))]; - 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(13585472)))]; - 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(13586560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14373056))))[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(14376192)))]; - 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(14379328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824))))[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(15166912)))]; - 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(15168000)))]; - 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(15175232)))]; - 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(15176320)))]; - 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(15177408)))]; - 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(15178496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15964992))))[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(15968128)))]; - 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(15971264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760))))[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(16758848)))]; - 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(16759936)))]; - 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(16767168)))]; - 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(16768256)))]; - 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(16769344)))]; - 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(16770432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17556928))))[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(17560064)))]; - 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(17563200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696))))[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(18350784)))]; - 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(18351872)))]; - 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(18359104)))]; - 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(18360192)))]; - 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(18361280)))]; - 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(18362368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19148864))))[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(19152000)))]; - 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(19155136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632))))[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(19942720)))]; - 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(19943808)))]; - 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(19951040)))]; - 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(19952128)))]; - 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(19953216)))]; - 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(19954304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20740800))))[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(20743936)))]; - 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(20747072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568))))[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(21534656)))]; - 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(21535744)))]; - 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(21542976)))]; - 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(21544064)))]; - 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(21545152)))]; - 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(21546240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22332736))))[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(22335872)))]; - 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(22339008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504))))[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(23126592)))]; - 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(23127680)))]; - 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(23134912)))]; - 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(23136000)))]; - 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(23137088)))]; - 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(23138176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23924672))))[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(23927808)))]; - 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(23930944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440))))[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(24718528)))]; - 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(24719616)))]; - 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(24726848)))]; - 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(24727936)))]; - 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(24729024)))]; - 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(24730112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25516608))))[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(25519744)))]; - 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(25522880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376))))[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(26310464)))]; - 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(26311552)))]; - 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(26312640)))]; - 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(26313728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104))))[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(26841280)))]; - 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(26843456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368832))))[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(27370944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896320))))[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(27898432)))]; - 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(27900544)))]; - 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 = const()[name = string("module_adapter_voice_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440)))]; - 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(10)]; - 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(10)]; - 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)[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(37184)))]; - 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(42624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47680))))[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(47808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785152))))[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(785984)))]; - 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(786816)))]; - 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(787648)))]; - 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(788480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[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(936832)))]; - 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(937664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675008))))[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(1675840)))]; - 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(1676672)))]; - 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(1677504)))]; - 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(1678336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825856))))[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(1826688)))]; - 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(1827520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564864))))[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(2565696)))]; - 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(2566528)))]; - 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(2567360)))]; - 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(2568192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715712))))[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(2716544)))]; - 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(2717376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454720))))[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(3455552)))]; - 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(3456384)))]; - 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(3457216)))]; - 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(3458048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605568))))[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(3606400)))]; - 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(3607232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344576))))[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(4345408)))]; - 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(4346240)))]; - 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(4347072)))]; - 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(4347904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4495424))))[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(4496256)))]; - 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(4497088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234432))))[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(5235264)))]; - 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(5236096)))]; - 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(5236928)))]; - 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(5237760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5385280))))[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(5386112)))]; - 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(5386944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124288))))[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(6125120)))]; - 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(6125952)))]; - 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(6126784)))]; - 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(6127616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6275136))))[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(6275968)))]; - 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(6276800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014144))))[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(7014976)))]; - 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(7015808)))]; - 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(7016640)))]; - 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(7017472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164992))))[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(7165824)))]; - 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(7166656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904000))))[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(7904832)))]; - 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(7905664)))]; - 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(7906496)))]; - 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(7907328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054848))))[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(8055680)))]; - 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(8056512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793856))))[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(8794688)))]; - 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(8795520)))]; - 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(8796352)))]; - 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(8797184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944704))))[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(8945536)))]; - 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(8946368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683712))))[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(9684544)))]; - 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(9685376)))]; - 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(9686208)))]; - 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(9687040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834560))))[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(9835392)))]; - 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(9836224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573568))))[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(10574400)))]; - 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(10575232)))]; - 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(10576064)))]; - 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(10576896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10724416))))[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(10725248)))]; - 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(10726080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315968))))[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(11316800)))]; - 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(11317632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054976))))[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(12055808)))]; - 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(12056640)))]; - 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(12057472)))]; - 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(12058304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205824))))[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(12206656)))]; - 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(12207488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944832))))[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(12945664)))]; - 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(12946496)))]; - 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(12947328)))]; - 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(12948160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095680))))[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(13096512)))]; - 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(13097344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13212608))))[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(13212928)))]; - 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(13213248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712))))[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(13572800)))]; - 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(13573888)))]; - 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(13574976)))]; - 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(13576064)))]; - 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(13583296)))]; - 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(13584384)))]; - 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(13585472)))]; - 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(13586560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14373056))))[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(14376192)))]; - 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(14379328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824))))[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(15166912)))]; - 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(15168000)))]; - 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(15175232)))]; - 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(15176320)))]; - 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(15177408)))]; - 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(15178496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15964992))))[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(15968128)))]; - 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(15971264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760))))[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(16758848)))]; - 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(16759936)))]; - 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(16767168)))]; - 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(16768256)))]; - 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(16769344)))]; - 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(16770432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17556928))))[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(17560064)))]; - 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(17563200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696))))[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(18350784)))]; - 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(18351872)))]; - 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(18359104)))]; - 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(18360192)))]; - 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(18361280)))]; - 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(18362368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19148864))))[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(19152000)))]; - 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(19155136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632))))[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(19942720)))]; - 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(19943808)))]; - 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(19951040)))]; - 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(19952128)))]; - 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(19953216)))]; - 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(19954304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20740800))))[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(20743936)))]; - 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(20747072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568))))[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(21534656)))]; - 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(21535744)))]; - 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(21542976)))]; - 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(21544064)))]; - 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(21545152)))]; - 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(21546240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22332736))))[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(22335872)))]; - 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(22339008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504))))[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(23126592)))]; - 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(23127680)))]; - 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(23134912)))]; - 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(23136000)))]; - 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(23137088)))]; - 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(23138176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23924672))))[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(23927808)))]; - 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(23930944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440))))[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(24718528)))]; - 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(24719616)))]; - 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(24726848)))]; - 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(24727936)))]; - 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(24729024)))]; - 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(24730112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25516608))))[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(25519744)))]; - 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(25522880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376))))[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(26310464)))]; - 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(26311552)))]; - 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(26312640)))]; - 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(26313728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104))))[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(26841280)))]; - 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(26843456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368832))))[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(27370944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896320))))[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(27898432)))]; - 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(28031168)))]; - 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 = const()[name = string("module_adapter_voice_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440)))]; - 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(10)]; - 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(10)]; - 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)[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(37184)))]; - 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(42624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47680))))[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(47808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785152))))[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(785984)))]; - 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(786816)))]; - 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(787648)))]; - 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(788480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[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(936832)))]; - 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(937664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675008))))[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(1675840)))]; - 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(1676672)))]; - 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(1677504)))]; - 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(1678336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825856))))[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(1826688)))]; - 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(1827520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564864))))[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(2565696)))]; - 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(2566528)))]; - 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(2567360)))]; - 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(2568192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715712))))[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(2716544)))]; - 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(2717376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454720))))[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(3455552)))]; - 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(3456384)))]; - 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(3457216)))]; - 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(3458048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605568))))[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(3606400)))]; - 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(3607232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344576))))[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(4345408)))]; - 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(4346240)))]; - 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(4347072)))]; - 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(4347904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4495424))))[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(4496256)))]; - 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(4497088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234432))))[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(5235264)))]; - 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(5236096)))]; - 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(5236928)))]; - 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(5237760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5385280))))[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(5386112)))]; - 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(5386944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124288))))[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(6125120)))]; - 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(6125952)))]; - 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(6126784)))]; - 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(6127616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6275136))))[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(6275968)))]; - 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(6276800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014144))))[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(7014976)))]; - 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(7015808)))]; - 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(7016640)))]; - 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(7017472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164992))))[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(7165824)))]; - 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(7166656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904000))))[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(7904832)))]; - 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(7905664)))]; - 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(7906496)))]; - 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(7907328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054848))))[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(8055680)))]; - 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(8056512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793856))))[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(8794688)))]; - 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(8795520)))]; - 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(8796352)))]; - 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(8797184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944704))))[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(8945536)))]; - 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(8946368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683712))))[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(9684544)))]; - 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(9685376)))]; - 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(9686208)))]; - 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(9687040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834560))))[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(9835392)))]; - 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(9836224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573568))))[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(10574400)))]; - 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(10575232)))]; - 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(10576064)))]; - 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(10576896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10724416))))[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(10725248)))]; - 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(10726080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315968))))[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(11316800)))]; - 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(11317632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054976))))[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(12055808)))]; - 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(12056640)))]; - 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(12057472)))]; - 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(12058304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205824))))[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(12206656)))]; - 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(12207488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944832))))[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(12945664)))]; - 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(12946496)))]; - 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(12947328)))]; - 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(12948160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095680))))[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(13096512)))]; - 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(13097344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13212608))))[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(13212928)))]; - 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(13213248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712))))[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(13572800)))]; - 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(13573888)))]; - 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(13574976)))]; - 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(13576064)))]; - 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(13583296)))]; - 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(13584384)))]; - 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(13585472)))]; - 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(13586560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14373056))))[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(14376192)))]; - 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(14379328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824))))[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(15166912)))]; - 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(15168000)))]; - 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(15175232)))]; - 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(15176320)))]; - 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(15177408)))]; - 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(15178496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15964992))))[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(15968128)))]; - 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(15971264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760))))[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(16758848)))]; - 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(16759936)))]; - 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(16767168)))]; - 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(16768256)))]; - 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(16769344)))]; - 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(16770432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17556928))))[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(17560064)))]; - 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(17563200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696))))[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(18350784)))]; - 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(18351872)))]; - 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(18359104)))]; - 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(18360192)))]; - 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(18361280)))]; - 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(18362368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19148864))))[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(19152000)))]; - 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(19155136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632))))[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(19942720)))]; - 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(19943808)))]; - 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(19951040)))]; - 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(19952128)))]; - 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(19953216)))]; - 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(19954304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20740800))))[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(20743936)))]; - 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(20747072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568))))[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(21534656)))]; - 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(21535744)))]; - 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(21542976)))]; - 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(21544064)))]; - 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(21545152)))]; - 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(21546240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22332736))))[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(22335872)))]; - 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(22339008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504))))[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(23126592)))]; - 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(23127680)))]; - 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(23134912)))]; - 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(23136000)))]; - 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(23137088)))]; - 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(23138176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23924672))))[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(23927808)))]; - 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(23930944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440))))[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(24718528)))]; - 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(24719616)))]; - 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(24726848)))]; - 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(24727936)))]; - 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(24729024)))]; - 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(24730112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25516608))))[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(25519744)))]; - 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(25522880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376))))[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(26310464)))]; - 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(26311552)))]; - 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(26312640)))]; - 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(26313728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104))))[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(26841280)))]; - 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(26843456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368832))))[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(27370944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896320))))[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(27898432)))]; - 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(28292864)))]; - 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 = const()[name = string("module_adapter_voice_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440)))]; - 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(10)]; - 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(10)]; - 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)[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(37184)))]; - 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(42624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47680))))[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(47808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785152))))[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(785984)))]; - 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(786816)))]; - 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(787648)))]; - 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(788480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[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(936832)))]; - 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(937664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675008))))[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(1675840)))]; - 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(1676672)))]; - 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(1677504)))]; - 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(1678336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825856))))[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(1826688)))]; - 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(1827520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564864))))[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(2565696)))]; - 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(2566528)))]; - 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(2567360)))]; - 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(2568192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715712))))[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(2716544)))]; - 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(2717376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454720))))[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(3455552)))]; - 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(3456384)))]; - 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(3457216)))]; - 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(3458048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605568))))[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(3606400)))]; - 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(3607232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344576))))[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(4345408)))]; - 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(4346240)))]; - 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(4347072)))]; - 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(4347904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4495424))))[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(4496256)))]; - 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(4497088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234432))))[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(5235264)))]; - 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(5236096)))]; - 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(5236928)))]; - 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(5237760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5385280))))[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(5386112)))]; - 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(5386944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124288))))[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(6125120)))]; - 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(6125952)))]; - 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(6126784)))]; - 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(6127616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6275136))))[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(6275968)))]; - 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(6276800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014144))))[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(7014976)))]; - 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(7015808)))]; - 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(7016640)))]; - 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(7017472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164992))))[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(7165824)))]; - 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(7166656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904000))))[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(7904832)))]; - 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(7905664)))]; - 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(7906496)))]; - 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(7907328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054848))))[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(8055680)))]; - 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(8056512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793856))))[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(8794688)))]; - 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(8795520)))]; - 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(8796352)))]; - 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(8797184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944704))))[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(8945536)))]; - 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(8946368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683712))))[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(9684544)))]; - 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(9685376)))]; - 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(9686208)))]; - 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(9687040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834560))))[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(9835392)))]; - 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(9836224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573568))))[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(10574400)))]; - 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(10575232)))]; - 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(10576064)))]; - 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(10576896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10724416))))[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(10725248)))]; - 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(10726080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315968))))[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(11316800)))]; - 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(11317632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054976))))[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(12055808)))]; - 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(12056640)))]; - 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(12057472)))]; - 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(12058304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205824))))[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(12206656)))]; - 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(12207488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944832))))[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(12945664)))]; - 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(12946496)))]; - 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(12947328)))]; - 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(12948160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095680))))[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(13096512)))]; - 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(13097344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13212608))))[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(13212928)))]; - 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(13213248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712))))[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(13572800)))]; - 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(13573888)))]; - 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(13574976)))]; - 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(13576064)))]; - 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(13583296)))]; - 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(13584384)))]; - 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(13585472)))]; - 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(13586560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14373056))))[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(14376192)))]; - 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(14379328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824))))[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(15166912)))]; - 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(15168000)))]; - 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(15175232)))]; - 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(15176320)))]; - 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(15177408)))]; - 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(15178496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15964992))))[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(15968128)))]; - 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(15971264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760))))[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(16758848)))]; - 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(16759936)))]; - 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(16767168)))]; - 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(16768256)))]; - 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(16769344)))]; - 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(16770432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17556928))))[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(17560064)))]; - 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(17563200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696))))[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(18350784)))]; - 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(18351872)))]; - 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(18359104)))]; - 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(18360192)))]; - 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(18361280)))]; - 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(18362368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19148864))))[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(19152000)))]; - 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(19155136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632))))[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(19942720)))]; - 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(19943808)))]; - 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(19951040)))]; - 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(19952128)))]; - 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(19953216)))]; - 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(19954304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20740800))))[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(20743936)))]; - 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(20747072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568))))[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(21534656)))]; - 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(21535744)))]; - 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(21542976)))]; - 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(21544064)))]; - 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(21545152)))]; - 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(21546240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22332736))))[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(22335872)))]; - 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(22339008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504))))[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(23126592)))]; - 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(23127680)))]; - 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(23134912)))]; - 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(23136000)))]; - 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(23137088)))]; - 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(23138176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23924672))))[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(23927808)))]; - 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(23930944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440))))[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(24718528)))]; - 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(24719616)))]; - 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(24726848)))]; - 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(24727936)))]; - 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(24729024)))]; - 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(24730112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25516608))))[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(25519744)))]; - 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(25522880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376))))[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(26310464)))]; - 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(26311552)))]; - 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(26312640)))]; - 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(26313728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104))))[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(26841280)))]; - 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(26843456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368832))))[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(27370944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896320))))[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(27898432)))]; - 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(28685632)))]; - 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 = const()[name = string("module_adapter_voice_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440)))]; - 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(10)]; - 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(10)]; - 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)[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(37184)))]; - 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(42624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47680))))[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(47808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785152))))[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(785984)))]; - 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(786816)))]; - 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(787648)))]; - 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(788480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[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(936832)))]; - 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(937664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675008))))[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(1675840)))]; - 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(1676672)))]; - 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(1677504)))]; - 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(1678336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825856))))[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(1826688)))]; - 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(1827520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564864))))[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(2565696)))]; - 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(2566528)))]; - 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(2567360)))]; - 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(2568192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715712))))[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(2716544)))]; - 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(2717376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454720))))[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(3455552)))]; - 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(3456384)))]; - 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(3457216)))]; - 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(3458048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605568))))[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(3606400)))]; - 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(3607232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344576))))[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(4345408)))]; - 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(4346240)))]; - 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(4347072)))]; - 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(4347904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4495424))))[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(4496256)))]; - 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(4497088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234432))))[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(5235264)))]; - 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(5236096)))]; - 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(5236928)))]; - 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(5237760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5385280))))[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(5386112)))]; - 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(5386944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124288))))[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(6125120)))]; - 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(6125952)))]; - 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(6126784)))]; - 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(6127616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6275136))))[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(6275968)))]; - 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(6276800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014144))))[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(7014976)))]; - 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(7015808)))]; - 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(7016640)))]; - 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(7017472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164992))))[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(7165824)))]; - 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(7166656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904000))))[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(7904832)))]; - 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(7905664)))]; - 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(7906496)))]; - 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(7907328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054848))))[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(8055680)))]; - 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(8056512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793856))))[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(8794688)))]; - 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(8795520)))]; - 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(8796352)))]; - 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(8797184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944704))))[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(8945536)))]; - 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(8946368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683712))))[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(9684544)))]; - 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(9685376)))]; - 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(9686208)))]; - 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(9687040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834560))))[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(9835392)))]; - 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(9836224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573568))))[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(10574400)))]; - 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(10575232)))]; - 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(10576064)))]; - 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(10576896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10724416))))[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(10725248)))]; - 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(10726080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315968))))[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(11316800)))]; - 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(11317632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054976))))[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(12055808)))]; - 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(12056640)))]; - 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(12057472)))]; - 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(12058304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205824))))[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(12206656)))]; - 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(12207488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944832))))[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(12945664)))]; - 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(12946496)))]; - 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(12947328)))]; - 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(12948160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095680))))[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(13096512)))]; - 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(13097344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13212608))))[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(13212928)))]; - 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(13213248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712))))[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(13572800)))]; - 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(13573888)))]; - 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(13574976)))]; - 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(13576064)))]; - 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(13583296)))]; - 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(13584384)))]; - 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(13585472)))]; - 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(13586560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14373056))))[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(14376192)))]; - 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(14379328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824))))[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(15166912)))]; - 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(15168000)))]; - 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(15175232)))]; - 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(15176320)))]; - 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(15177408)))]; - 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(15178496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15964992))))[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(15968128)))]; - 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(15971264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760))))[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(16758848)))]; - 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(16759936)))]; - 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(16767168)))]; - 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(16768256)))]; - 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(16769344)))]; - 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(16770432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17556928))))[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(17560064)))]; - 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(17563200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696))))[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(18350784)))]; - 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(18351872)))]; - 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(18359104)))]; - 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(18360192)))]; - 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(18361280)))]; - 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(18362368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19148864))))[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(19152000)))]; - 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(19155136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632))))[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(19942720)))]; - 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(19943808)))]; - 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(19951040)))]; - 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(19952128)))]; - 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(19953216)))]; - 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(19954304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20740800))))[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(20743936)))]; - 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(20747072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568))))[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(21534656)))]; - 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(21535744)))]; - 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(21542976)))]; - 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(21544064)))]; - 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(21545152)))]; - 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(21546240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22332736))))[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(22335872)))]; - 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(22339008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504))))[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(23126592)))]; - 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(23127680)))]; - 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(23134912)))]; - 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(23136000)))]; - 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(23137088)))]; - 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(23138176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23924672))))[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(23927808)))]; - 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(23930944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440))))[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(24718528)))]; - 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(24719616)))]; - 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(24726848)))]; - 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(24727936)))]; - 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(24729024)))]; - 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(24730112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25516608))))[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(25519744)))]; - 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(25522880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376))))[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(26310464)))]; - 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(26311552)))]; - 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(26312640)))]; - 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(26313728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104))))[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(26841280)))]; - 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(26843456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368832))))[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(27370944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896320))))[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(27898432)))]; - 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(29209472)))]; - 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 deleted file mode 100644 index c9b3863ca78a5ca0bf6dc6334cc229a57acafd7e..0000000000000000000000000000000000000000 --- a/coreml/coreml/vocoder.mlmodelc/weights/weight.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:8d1aa1e85be960d5ad32f751b602c38601bc9517a951be5bd42c991449d3bccc -size 31043584 diff --git a/coreml/manifest.json b/coreml/manifest.json deleted file mode 100644 index 4724a48ddfed9919993567aaadd687c17b5cfa78..0000000000000000000000000000000000000000 --- a/coreml/manifest.json +++ /dev/null @@ -1,998 +0,0 @@ -{ - "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 - }, - "g2p_256": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/g2p.mlmodelc", - "function": "g2p_256" - } - }, - "inputs": [ - "text" - ], - "outputs": [ - "logits" - ], - "required": false - }, - "g2p": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/g2p.mlmodelc", - "function": "g2p" - } - }, - "inputs": [ - "text" - ], - "outputs": [ - "logits" - ], - "required": true - }, - "duration_predictor": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/duration_predictor.mlmodelc" - } - }, - "inputs": [ - "span_phone_ids", - "span_segment_ids", - "span_mask", - "voice_id", - "language_id", - "delivery_values", - "delivery_present" - ], - "outputs": [ - "durations" - ], - "required": true - }, - "vector_context_encoder": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vector_context_encoder.mlmodelc" - } - }, - "inputs": [ - "span_context_phone_ids", - "span_context_segment_ids", - "span_context_mask" - ], - "outputs": [ - "span_context_hidden" - ], - "required": true - }, - "vector_estimator_128": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vector_estimator.mlmodelc", - "function": "vector_estimator_128" - } - }, - "inputs": [ - "noise", - "time", - "expanded_phone_ids", - "voice_id", - "language_id", - "delivery_values", - "delivery_present", - "latent_mask", - "span_context_hidden", - "prefix_latents", - "prefix_mask", - "expanded_boundary_event_ids", - "expanded_modifier_event_ids", - "expanded_phone_phase", - "expanded_phone_log_duration", - "expanded_sentence_type_ids" - ], - "outputs": [ - "velocity" - ], - "required": false - }, - "vocoder_128": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vocoder.mlmodelc", - "function": "vocoder_128" - } - }, - "inputs": [ - "latents", - "latent_mask", - "voice_id", - "language_id", - "emotion_id" - ], - "outputs": [ - "audio" - ], - "required": false - }, - "vector_estimator_256": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vector_estimator.mlmodelc", - "function": "vector_estimator_256" - } - }, - "inputs": [ - "noise", - "time", - "expanded_phone_ids", - "voice_id", - "language_id", - "delivery_values", - "delivery_present", - "latent_mask", - "span_context_hidden", - "prefix_latents", - "prefix_mask", - "expanded_boundary_event_ids", - "expanded_modifier_event_ids", - "expanded_phone_phase", - "expanded_phone_log_duration", - "expanded_sentence_type_ids" - ], - "outputs": [ - "velocity" - ], - "required": false - }, - "vocoder_256": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vocoder.mlmodelc", - "function": "vocoder_256" - } - }, - "inputs": [ - "latents", - "latent_mask", - "voice_id", - "language_id", - "emotion_id" - ], - "outputs": [ - "audio" - ], - "required": false - }, - "vector_estimator_384": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vector_estimator.mlmodelc", - "function": "vector_estimator_384" - } - }, - "inputs": [ - "noise", - "time", - "expanded_phone_ids", - "voice_id", - "language_id", - "delivery_values", - "delivery_present", - "latent_mask", - "span_context_hidden", - "prefix_latents", - "prefix_mask", - "expanded_boundary_event_ids", - "expanded_modifier_event_ids", - "expanded_phone_phase", - "expanded_phone_log_duration", - "expanded_sentence_type_ids" - ], - "outputs": [ - "velocity" - ], - "required": false - }, - "vocoder_384": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vocoder.mlmodelc", - "function": "vocoder_384" - } - }, - "inputs": [ - "latents", - "latent_mask", - "voice_id", - "language_id", - "emotion_id" - ], - "outputs": [ - "audio" - ], - "required": false - }, - "vector_estimator_512": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vector_estimator.mlmodelc", - "function": "vector_estimator_512" - } - }, - "inputs": [ - "noise", - "time", - "expanded_phone_ids", - "voice_id", - "language_id", - "delivery_values", - "delivery_present", - "latent_mask", - "span_context_hidden", - "prefix_latents", - "prefix_mask", - "expanded_boundary_event_ids", - "expanded_modifier_event_ids", - "expanded_phone_phase", - "expanded_phone_log_duration", - "expanded_sentence_type_ids" - ], - "outputs": [ - "velocity" - ], - "required": false - }, - "vocoder_512": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vocoder.mlmodelc", - "function": "vocoder_512" - } - }, - "inputs": [ - "latents", - "latent_mask", - "voice_id", - "language_id", - "emotion_id" - ], - "outputs": [ - "audio" - ], - "required": false - }, - "vector_estimator_768": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vector_estimator.mlmodelc", - "function": "vector_estimator_768" - } - }, - "inputs": [ - "noise", - "time", - "expanded_phone_ids", - "voice_id", - "language_id", - "delivery_values", - "delivery_present", - "latent_mask", - "span_context_hidden", - "prefix_latents", - "prefix_mask", - "expanded_boundary_event_ids", - "expanded_modifier_event_ids", - "expanded_phone_phase", - "expanded_phone_log_duration", - "expanded_sentence_type_ids" - ], - "outputs": [ - "velocity" - ], - "required": false - }, - "vocoder_768": { - "artifacts": { - "coreml": { - "format": "coreml", - "path": "coreml/vocoder.mlmodelc", - "function": "vocoder_768" - 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b5ee729b27aa83bdd20342937b1ad82408cecadd..f62fb6a714623d367f045bd8452a079b2340fd19 100644 --- a/litert/assets/voices.json +++ b/litert/assets/voices.json @@ -38,5 +38,21 @@ { "id": "tuesday", "index": 9 + }, + { + "id": "thistle", + "index": 10 + }, + { + "id": "kincade", + "index": 11 + }, + { + "id": "poppy", + "index": 12 + }, + { + "id": "priya", + "index": 13 } ] diff --git a/litert/litert/duration_predictor.tflite b/litert/litert/duration_predictor.tflite index bcd6e9768dd7f280f1ee2ad28215a9cc27e051b4..a4abd809ef3eac2252768ee77f67d6f2eab74a6f 100644 --- a/litert/litert/duration_predictor.tflite +++ b/litert/litert/duration_predictor.tflite @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a7b063e6121ca62bc7e4940822d1016c47999c1688bd7482b27cd7396d5b5bab -size 11599200 +oid sha256:02d815abcae4b3915f7569a1796364a5b7d70adc8e928fc2b7beb33566f6b444 +size 11668736 diff --git a/litert/litert/vector_context_encoder.tflite b/litert/litert/vector_context_encoder.tflite index eeaa9abc22c194ead637d2dd1c8c96255c70cfd8..17fb7f7faa837a58246a1263018dd9ff2d9d7e2a 100644 --- a/litert/litert/vector_context_encoder.tflite +++ b/litert/litert/vector_context_encoder.tflite @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a7d653507b92d8a5102f4281361e8ee8f03cf001b6889e1434ad1e760fbcf057 -size 7763584 +oid sha256:de37ff6548202785b9a7c3edac609b9d94d11f437cc959bcfff61fc909272e5e +size 11894656 diff --git a/litert/litert/vector_estimator.tflite b/litert/litert/vector_estimator.tflite index 8c7e183782e8f85b46ac18c8b1816f250de4c846..fd6af856517c450c982fce084265c71954a8e0a5 100644 --- a/litert/litert/vector_estimator.tflite +++ b/litert/litert/vector_estimator.tflite @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:51f6ce100d8565fa7979718ca34ad0b19d1e14f6525c41c46112dfbd1d5b30cd -size 76550528 +oid sha256:cebf54a5b5009c4bf47982cf370a61b36bf3279ea8c16f7c0d974a4e5534ed56 +size 146540928 diff --git a/litert/litert/vocoder.tflite b/litert/litert/vocoder.tflite index 2ae7f12d2348123e2b89d4d11882265755ce5bc8..10e316f728989d80463d6062cdcdea78a11088e0 100644 --- a/litert/litert/vocoder.tflite +++ b/litert/litert/vocoder.tflite @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a0410221c7742098fa5efea7f6f7626e522d3a761d84d8bf2e811c9d0de534f3 -size 38613392 +oid sha256:bc04828ad6575713d597585b117081c2f0af6185b6ea9eee6c979dd6b94cbf74 +size 38615216 diff --git a/litert/manifest.json b/litert/manifest.json index e3db5baf4a8d49884ce526ace2e56f89247d248c..8d656d5ebefb8360788cdb4cc1a6a2e0540a7ed7 100644 --- a/litert/manifest.json +++ b/litert/manifest.json @@ -447,7 +447,7 @@ 0.0, 4.0 ], - "release_id": "v2-20261006", + "release_id": "v2-20261020", "scope": "utterance", "timeline_conditioning": false, "voice_locales": { @@ -483,6 +483,14 @@ "it", "vi" ], + "kincade": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], "max": [ "de", "en_us", @@ -499,6 +507,22 @@ "it", "vi" ], + "poppy": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], + "priya": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], "rex": [ "de", "en_us", @@ -523,6 +547,14 @@ "it", "vi" ], + "thistle": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], "tuesday": [ "de", "en_gb", @@ -557,19 +589,19 @@ "quantization": "dynamic_wi8_afp32", "report": { "duration_predictor": { - "bytes": 11599200, + "bytes": 11668736, "quantized": "dynamic_wi8_afp32" }, "vector_context_encoder": { - "bytes": 7763584, + "bytes": 11894656, "quantized": "dynamic_wi8_afp32" }, "vector_estimator": { - "bytes": 76550528, + "bytes": 146540928, "quantized": "dynamic_wi8_afp32" }, "vocoder": { - "bytes": 38613392, + "bytes": 38615216, "quantized": "dynamic_wi8_afp32" } }, @@ -623,7 +655,7 @@ "exclude_context_phones": [ "" ], - "hidden_size": 512, + "hidden_size": 640, "layout": "balanced_before_target_after", "scope": "passage", "segments": 3, @@ -726,7 +758,7 @@ }, "dataset_summary": { "controls": "measured_four_axes", - "voice_locale_pairs": 60 + "voice_locale_pairs": 84 }, "default_language": "en_us", "languages": [ @@ -913,6 +945,74 @@ "styles": [ "default" ] + }, + { + "default_language": "en_us", + "embedding_path": null, + "id": "thistle", + "languages": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], + "name": "thistle", + "styles": [ + "default" + ] + }, + { + "default_language": "en_us", + "embedding_path": null, + "id": "kincade", + "languages": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], + "name": "kincade", + "styles": [ + "default" + ] + }, + { + "default_language": "en_us", + "embedding_path": null, + "id": "poppy", + "languages": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], + "name": "poppy", + "styles": [ + "default" + ] + }, + { + "default_language": "en_us", + "embedding_path": null, + "id": "priya", + "languages": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], + "name": "priya", + "styles": [ + "default" + ] } ] } 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1.0, diff --git a/onnx/assets/voices.json b/onnx/assets/voices.json index b5ee729b27aa83bdd20342937b1ad82408cecadd..f62fb6a714623d367f045bd8452a079b2340fd19 100644 --- a/onnx/assets/voices.json +++ b/onnx/assets/voices.json @@ -38,5 +38,21 @@ { "id": "tuesday", "index": 9 + }, + { + "id": "thistle", + "index": 10 + }, + { + "id": "kincade", + "index": 11 + }, + { + "id": "poppy", + "index": 12 + }, + { + "id": "priya", + "index": 13 } ] diff --git a/onnx/manifest.json b/onnx/manifest.json index b8ce7d1f43c4466bd31066d38f0bd9c59f16f15e..bd2cbf01c7acf6d68d5e2ead4bd6fec0e4517840 100644 --- a/onnx/manifest.json +++ b/onnx/manifest.json @@ -432,7 +432,7 @@ 0.0, 4.0 ], - "release_id": "v2-20261006", + "release_id": "v2-20261020", "scope": "utterance", "timeline_conditioning": false, "voice_locales": { @@ -468,6 +468,14 @@ "it", "vi" ], + "kincade": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], "max": [ "de", "en_us", @@ -484,6 +492,22 @@ "it", "vi" ], + "poppy": [ + "en_us", + 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"length": 2048, "location": "shared_weights.bin" }, @@ -29168,7 +34904,7 @@ 7 ], "data_type": 1, - "offset": 43346688, + "offset": 47229056, "length": 14336, "location": "shared_weights.bin" }, @@ -29181,7 +34917,7 @@ 512 ], "data_type": 1, - "offset": 43342592, + "offset": 47224960, "length": 2048, "location": "shared_weights.bin" }, @@ -29194,7 +34930,7 @@ 512 ], "data_type": 1, - "offset": 136889408, + "offset": 178604480, "length": 2048, "location": "shared_weights.bin" }, @@ -29207,7 +34943,7 @@ 1536 ], "data_type": 1, - "offset": 158178112, + "offset": 203982272, "length": 6144, "location": "shared_weights.bin" }, @@ -29220,7 +34956,7 @@ 512 ], "data_type": 1, - "offset": 64164928, + "offset": 85422784, "length": 2048, "location": "shared_weights.bin" }, @@ -29233,7 +34969,7 @@ 512 ], "data_type": 1, - "offset": 79873088, + "offset": 104989376, "length": 2048, "location": "shared_weights.bin" }, @@ -29248,7 +34984,7 @@ 7 ], "data_type": 1, - "offset": 164191040, + "offset": 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}, @@ -36113,7 +41849,7 @@ 512 ], "data_type": 1, - "offset": 60532544, + "offset": 81554880, "length": 2048, "location": "shared_weights.bin" }, @@ -36139,7 +41875,7 @@ 512 ], "data_type": 1, - "offset": 142501696, + "offset": 185087168, "length": 2048, "location": "shared_weights.bin" }, @@ -36152,7 +41888,7 @@ 512 ], "data_type": 1, - "offset": 122868288, + "offset": 162902464, "length": 2048, "location": "shared_weights.bin" }, @@ -36167,7 +41903,7 @@ 7 ], "data_type": 1, - "offset": 109711424, + "offset": 150290368, "length": 14336, "location": "shared_weights.bin" }, @@ -36180,7 +41916,7 @@ 512 ], "data_type": 1, - "offset": 176910400, + "offset": 235497664, "length": 2048, "location": "shared_weights.bin" }, @@ -36193,7 +41929,7 @@ 512 ], "data_type": 1, - "offset": 190746688, + "offset": 255866560, "length": 2048, "location": "shared_weights.bin" }, @@ -36206,7 +41942,7 @@ 1536 ], "data_type": 1, - "offset": 131099968, + "offset": 173134528, "length": 6144, "location": 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"length": 2048, "location": "shared_weights.bin" }, @@ -36407,7 +42143,7 @@ 7 ], "data_type": 1, - "offset": 43346688, + "offset": 47229056, "length": 14336, "location": "shared_weights.bin" }, @@ -36420,7 +42156,7 @@ 512 ], "data_type": 1, - "offset": 43342592, + "offset": 47224960, "length": 2048, "location": "shared_weights.bin" }, @@ -36433,7 +42169,7 @@ 512 ], "data_type": 1, - "offset": 136889408, + "offset": 178604480, "length": 2048, "location": "shared_weights.bin" }, @@ -36446,7 +42182,7 @@ 1536 ], "data_type": 1, - "offset": 158178112, + "offset": 203982272, "length": 6144, "location": "shared_weights.bin" }, @@ -36459,7 +42195,7 @@ 512 ], "data_type": 1, - "offset": 64164928, + "offset": 85422784, "length": 2048, "location": "shared_weights.bin" }, @@ -36472,7 +42208,7 @@ 512 ], "data_type": 1, - "offset": 79873088, + "offset": 104989376, "length": 2048, "location": "shared_weights.bin" }, @@ -36487,7 +42223,7 @@ 7 ], "data_type": 1, - "offset": 164191040, + "offset": 217364672, "length": 14336, "location": "shared_weights.bin" }, @@ -36500,7 +42236,7 @@ 512 ], "data_type": 1, - "offset": 126458432, + "offset": 168940736, "length": 2048, "location": "shared_weights.bin" }, @@ -36513,7 +42249,7 @@ 512 ], "data_type": 1, - "offset": 207160896, + "offset": 277084096, "length": 2048, "location": "shared_weights.bin" }, @@ -36526,7 +42262,7 @@ 1536 ], "data_type": 1, - "offset": 173739072, + "offset": 232327360, "length": 6144, "location": "shared_weights.bin" }, @@ -36539,7 +42275,7 @@ 512 ], "data_type": 1, - "offset": 112999232, + "offset": 152604608, "length": 2048, "location": "shared_weights.bin" }, @@ -36552,7 +42288,7 @@ 512 ], "data_type": 1, - "offset": 113028672, + "offset": 153021888, "length": 2048, "location": "shared_weights.bin" }, @@ -36567,7 +42303,7 @@ 7 ], "data_type": 1, - "offset": 121651264, + "offset": 162137024, "length": 14336, "location": "shared_weights.bin" }, @@ -36580,7 +42316,7 @@ 512 ], "data_type": 1, - "offset": 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b/pytorch/condition_index.json @@ -1,43 +1,49 @@ { - "schema_version": 1, - "prepared_dir": "output/scyllasband/scyllasband_v2_clean_202608/prepared", - "voice_ids": [ - "ariadne", - "felix", - "gwen", - "ink", - "max", - "orpheus", - "rex", - "scylla", - "stone", - "tuesday" - ], - "language_ids": [ - "en_us", - "en_gb", - "es", - "it", - "fr", - "de", - "vi" - ], - "emotion_ids": [ - "neutral", - "friendly", - "happy", - "excited", - "angry", - "frustrated", - "sad", - "sarcastic", - "unsure", - "anger", - "calm", - "joy", - "sadness" - ], - "voices": 10, - "languages": 7, - "emotions": 13 -} + "schema_version": 1, + "prepared_dir": "output/scyllasband/scyllasband_v2_clean_202608/prepared", + "voice_ids": [ + "ariadne", + "felix", + "gwen", + "ink", + "max", + "orpheus", + "rex", + "scylla", + "stone", + "tuesday", + "thistle", + "kincade", + "poppy", + "priya" + ], + "language_ids": [ + "en_us", + "en_gb", + "es", + "it", + "fr", + "de", + "vi" + ], + "emotion_ids": [ + "neutral", + "friendly", + "happy", + "excited", + "angry", + "frustrated", + "sad", + "sarcastic", + "unsure", + "anger", + "calm", + "joy", + "sadness" + ], + "voices": 14, + "languages": 7, + "emotions": 13, + "extended_from": "/media/sean/data/aiwork/output/scyllasband/full_retrain_20261010/assets/condition_index.json", + "note": "Poppy and Priya appended as voice ids 12 and 13 (Oct 17 2026)" +} \ No newline at end of file diff --git a/pytorch/duration.pt b/pytorch/duration.pt index 3b2a8158b8388634f31c8bbe0cd75fb7d161a3c5..1f900d393e0aa7f223a500b8ee08cc2c149cb742 100644 --- a/pytorch/duration.pt +++ b/pytorch/duration.pt @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:765e8970c5c014be982a0645129682b47338656ec407373683da78318a77a5dd -size 43039620 +oid sha256:6ca5a04e18924144b9658f0f1d9da271ba1974cab4bd2cf0362b08ed39ef6033 +size 43044740 diff --git a/pytorch/fixed_references.json b/pytorch/fixed_references.json index 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1.0, diff --git a/coreai/assets/g2p/export_config.json b/webgpu/assets/g2p/export_config.json similarity index 100% rename from coreai/assets/g2p/export_config.json rename to webgpu/assets/g2p/export_config.json diff --git a/coreai/assets/g2p/language_map.json b/webgpu/assets/g2p/language_map.json similarity index 100% rename from coreai/assets/g2p/language_map.json rename to webgpu/assets/g2p/language_map.json diff --git a/coreai/assets/g2p/normalization.json b/webgpu/assets/g2p/normalization.json similarity index 100% rename from coreai/assets/g2p/normalization.json rename to webgpu/assets/g2p/normalization.json diff --git a/coreai/assets/g2p/phoneme_dict.json b/webgpu/assets/g2p/phoneme_dict.json similarity index 100% rename from coreai/assets/g2p/phoneme_dict.json rename to webgpu/assets/g2p/phoneme_dict.json diff --git a/coreai/assets/g2p/pronunciation_overrides.json b/webgpu/assets/g2p/pronunciation_overrides.json similarity index 100% rename from coreai/assets/g2p/pronunciation_overrides.json rename to webgpu/assets/g2p/pronunciation_overrides.json diff --git a/coreai/assets/g2p/tokenizer.json b/webgpu/assets/g2p/tokenizer.json similarity index 100% rename from coreai/assets/g2p/tokenizer.json rename to webgpu/assets/g2p/tokenizer.json diff --git a/coreai/assets/languages.json b/webgpu/assets/languages.json similarity index 100% rename from coreai/assets/languages.json rename to webgpu/assets/languages.json diff --git a/coreai/assets/phone_vocab.json b/webgpu/assets/phone_vocab.json similarity index 100% rename from coreai/assets/phone_vocab.json rename to webgpu/assets/phone_vocab.json diff --git a/coreai/assets/voices.json b/webgpu/assets/voices.json similarity index 70% rename from coreai/assets/voices.json rename to webgpu/assets/voices.json index b5ee729b27aa83bdd20342937b1ad82408cecadd..f62fb6a714623d367f045bd8452a079b2340fd19 100644 --- a/coreai/assets/voices.json +++ b/webgpu/assets/voices.json @@ -38,5 +38,21 @@ { "id": "tuesday", "index": 9 + }, + { + "id": "thistle", + "index": 10 + }, + { + "id": "kincade", + "index": 11 + }, + { + "id": "poppy", + "index": 12 + }, + { + "id": "priya", + "index": 13 } ] diff --git a/coreai/manifest.json b/webgpu/manifest.json similarity index 76% rename from coreai/manifest.json rename to webgpu/manifest.json index 979064b7a1d59055e0a01db9487b8b994304fa40..5150e412c7cc38ebd2de7775ba8cdf3dc6dcbf21 100644 --- a/coreai/manifest.json +++ b/webgpu/manifest.json @@ -20,60 +20,32 @@ "sample_rate": 24000 }, "components": { - "g2p_64": { - "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/g2p.aimodel", - "function": "g2p_64" - } - }, - "inputs": [ - "text" - ], - "outputs": [ - "logits" - ], - "required": false - }, - "g2p_128": { - "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/g2p.aimodel", - "function": "g2p_128" - } - }, - "inputs": [ - "text" - ], - "outputs": [ - "logits" - ], - "required": false - }, - "g2p_256": { + "duration_predictor": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/g2p.aimodel", - "function": "g2p_256" + "onnx": { + "format": "onnx", + "path": "onnx/components/duration_predictor.onnx" } }, "inputs": [ - "text" + "span_phone_ids", + "span_segment_ids", + "span_mask", + "voice_id", + "language_id", + "delivery_values", + "delivery_present" ], "outputs": [ - "logits" + "durations" ], - "required": false + "required": true }, "g2p": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/g2p.aimodel", - "function": "g2p" + "onnx": { + "format": "onnx", + "path": "onnx/g2p/model.onnx" } }, "inputs": [ @@ -84,34 +56,11 @@ ], "required": true }, - "duration_predictor": { - "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/duration_predictor.aimodel", - "function": "duration_predictor" - } - }, - "inputs": [ - "span_phone_ids", - "span_segment_ids", - "span_mask", - "voice_id", - "language_id", - "delivery_values", - "delivery_present" - ], - "outputs": [ - "durations" - ], - "required": true - }, "vector_context_encoder": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vector_context_encoder.aimodel", - "function": "vector_context_encoder" + "onnx": { + "format": "onnx", + "path": "onnx/components/vector_context_encoder.onnx" } }, "inputs": [ @@ -124,12 +73,11 @@ ], "required": true }, - "vector_estimator_128": { + "vector_estimator": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vector_estimator.aimodel", - "function": "vector_estimator_128" + "onnx": { + "format": "onnx", + "path": "onnx/components/vector_estimator.onnx" } }, "inputs": [ @@ -153,34 +101,43 @@ "outputs": [ "velocity" ], - "required": false + "required": true }, - "vocoder_128": { + "vector_estimator_128": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vocoder.aimodel", - "function": "vocoder_128" + "onnx": { + "format": "onnx", + "path": "onnx/components/vector_estimator_128.onnx" } }, "inputs": [ - "latents", - "latent_mask", + "noise", + "time", + "expanded_phone_ids", "voice_id", "language_id", - "emotion_id" + "delivery_values", + "delivery_present", + "latent_mask", + "span_context_hidden", + "prefix_latents", + "prefix_mask", + "expanded_boundary_event_ids", + "expanded_modifier_event_ids", + "expanded_phone_phase", + "expanded_phone_log_duration", + "expanded_sentence_type_ids" ], "outputs": [ - "audio" + "velocity" ], "required": false }, "vector_estimator_256": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vector_estimator.aimodel", - "function": "vector_estimator_256" + "onnx": { + "format": "onnx", + "path": "onnx/components/vector_estimator_256.onnx" } }, "inputs": [ @@ -206,32 +163,11 @@ ], "required": false }, - "vocoder_256": { - "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vocoder.aimodel", - "function": "vocoder_256" - } - }, - "inputs": [ - "latents", - "latent_mask", - "voice_id", - "language_id", - "emotion_id" - ], - "outputs": [ - "audio" - ], - "required": false - }, "vector_estimator_384": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vector_estimator.aimodel", - "function": "vector_estimator_384" + "onnx": { + "format": "onnx", + "path": "onnx/components/vector_estimator_384.onnx" } }, "inputs": [ @@ -257,32 +193,41 @@ ], "required": false }, - "vocoder_384": { + "vector_estimator_512": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vocoder.aimodel", - "function": "vocoder_384" + "onnx": { + "format": "onnx", + "path": "onnx/components/vector_estimator_512.onnx" } }, "inputs": [ - "latents", - "latent_mask", + "noise", + "time", + "expanded_phone_ids", "voice_id", "language_id", - "emotion_id" + "delivery_values", + "delivery_present", + "latent_mask", + "span_context_hidden", + "prefix_latents", + "prefix_mask", + "expanded_boundary_event_ids", + "expanded_modifier_event_ids", + "expanded_phone_phase", + "expanded_phone_log_duration", + "expanded_sentence_type_ids" ], "outputs": [ - "audio" + "velocity" ], "required": false }, - "vector_estimator_512": { + "vector_estimator_768": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vector_estimator.aimodel", - "function": "vector_estimator_512" + "onnx": { + "format": "onnx", + "path": "onnx/components/vector_estimator_768.onnx" } }, "inputs": [ @@ -308,12 +253,11 @@ ], "required": false }, - "vocoder_512": { + "vocoder": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vocoder.aimodel", - "function": "vocoder_512" + "onnx": { + "format": "onnx", + "path": "onnx/components/vocoder.onnx" } }, "inputs": [ @@ -326,45 +270,32 @@ "outputs": [ "audio" ], - "required": false + "required": true }, - "vector_estimator_768": { + "vocoder_128": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vector_estimator.aimodel", - "function": "vector_estimator_768" + "onnx": { + "format": "onnx", + "path": "onnx/components/vocoder_128.onnx" } }, "inputs": [ - "noise", - "time", - "expanded_phone_ids", + "latents", + "latent_mask", "voice_id", "language_id", - "delivery_values", - "delivery_present", - "latent_mask", - "span_context_hidden", - "prefix_latents", - "prefix_mask", - "expanded_boundary_event_ids", - "expanded_modifier_event_ids", - "expanded_phone_phase", - "expanded_phone_log_duration", - "expanded_sentence_type_ids" + "emotion_id" ], "outputs": [ - "velocity" + "audio" ], "required": false }, - "vocoder_768": { + "vocoder_256": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vocoder.aimodel", - "function": "vocoder_768" + "onnx": { + "format": "onnx", + "path": "onnx/components/vocoder_256.onnx" } }, "inputs": [ @@ -379,43 +310,49 @@ ], "required": false }, - "vector_estimator": { + "vocoder_384": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vector_estimator.aimodel", - "function": "vector_estimator" + "onnx": { + "format": "onnx", + "path": "onnx/components/vocoder_384.onnx" } }, "inputs": [ - "noise", - "time", - "expanded_phone_ids", + "latents", + "latent_mask", "voice_id", "language_id", - "delivery_values", - "delivery_present", + "emotion_id" + ], + "outputs": [ + "audio" + ], + "required": false + }, + "vocoder_512": { + "artifacts": { + "onnx": { + "format": "onnx", + "path": "onnx/components/vocoder_512.onnx" + } + }, + "inputs": [ + "latents", "latent_mask", - "span_context_hidden", - "prefix_latents", - "prefix_mask", - "expanded_boundary_event_ids", - "expanded_modifier_event_ids", - "expanded_phone_phase", - "expanded_phone_log_duration", - "expanded_sentence_type_ids" + "voice_id", + "language_id", + "emotion_id" ], "outputs": [ - "velocity" + "audio" ], - "required": true + "required": false }, - "vocoder": { + "vocoder_768": { "artifacts": { - "coreai": { - "format": "coreai", - "path": "coreai/vocoder.aimodel", - "function": "vocoder" + "onnx": { + "format": "onnx", + "path": "onnx/components/vocoder_768.onnx" } }, "inputs": [ @@ -428,7 +365,7 @@ "outputs": [ "audio" ], - "required": true + "required": false } }, "contract_version": "1.0.0", @@ -495,7 +432,7 @@ 0.0, 4.0 ], - "release_id": "v2-20261006", + "release_id": "v2-20261020", "scope": "utterance", "timeline_conditioning": false, "voice_locales": { @@ -531,6 +468,14 @@ "it", "vi" ], + "kincade": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], "max": [ "de", "en_us", @@ -547,6 +492,22 @@ "it", "vi" ], + "poppy": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], + "priya": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], "rex": [ "de", "en_us", @@ -571,6 +532,14 @@ "it", "vi" ], + "thistle": [ + "en_us", + "es", + "de", + "fr", + "it", + "vi" + ], "tuesday": [ "de", "en_gb", @@ -601,6 +570,76 @@ "phone_frames": 512 }, "graph_input_contract": "scyllasband_measured_delivery_v2", + "onnx": { + "external_data": true, + "opset": 18, + "providers_validated": [ + "CPUExecutionProvider" + ], + "shared_initializer_map": "onnx/components/shared_weights.json", + "shared_weights": "onnx/components/shared_weights.bin", + "shared_weights_bytes": 284376512, + "quantization": { + "format": "onnx_weight_only_int8", + "weight_type": "QInt8", + "per_channel": true, + "profile": "core_ada", + "vocoder": "float32", + "g2p": "weight_only_int8", + "components": { + "duration_predictor": { + "int8_nodes": 38, + "method": "weight_only" + }, + "vector_context_encoder": { + "int8_nodes": 9, + "method": "weight_only" + }, + "vector_estimator": { + "int8_nodes": 93, + "method": "weight_only" + }, + "vector_estimator_128": { + "int8_nodes": 93, + "method": "weight_only" + }, + "vector_estimator_256": { + "int8_nodes": 93, + "method": "weight_only" + }, + "vector_estimator_384": { + "int8_nodes": 93, + "method": "weight_only" + }, + "vector_estimator_512": { + "int8_nodes": 93, + "method": "weight_only" + }, + "vector_estimator_768": { + "int8_nodes": 93, + "method": "weight_only" + }, + "vocoder": { + "int8_nodes": 0 + }, + "vocoder_128": { + "int8_nodes": 0 + }, + "vocoder_256": { + "int8_nodes": 0 + }, + "vocoder_384": { + "int8_nodes": 0 + }, + "vocoder_512": { + "int8_nodes": 0 + }, + "vocoder_768": { + "int8_nodes": 0 + } + } + } + }, "pitch": { "default": 0.0, "max": 4.0, @@ -647,7 +686,7 @@ "exclude_context_phones": [ "" ], - "hidden_size": 512, + "hidden_size": 640, "layout": "balanced_before_target_after", "scope": "passage", "segments": 3, @@ -722,12 +761,12 @@ "expanded_sentence_type_ids" ], "modifier_bits": { - "ˈ": 1, - "ˌ": 2, - "ː": 4, - "̃": 8, - "̩": 16, - "̪": 32 + "\u02c8": 1, + "\u02cc": 2, + "\u02d0": 4, + "\u0303": 8, + "\u0329": 16, + "\u032a": 32 }, "punctuation_owner": "following_silence_else_first_frame_of_next_phone", "sentence_type_count": 5, @@ -746,71 +785,11 @@ "enabled": true, "g2p_output_symbol": " ", "zero_frames_allowed": true - }, - "g2p_buckets": [ - { - "text_tokens": 64, - "component": "g2p_64" - }, - { - "text_tokens": 128, - "component": "g2p_128" - }, - { - "text_tokens": 256, - "component": "g2p_256" - }, - { - "text_tokens": 512, - "component": "g2p" - } - ], - "coreai": { - "minimum_os": { - "ios": "27.0", - "macos": "27.0" - }, - "io": { - "ids": "int32", - "masks": "int32", - "values": "float32" - }, - "assets": { - "g2p": { - "precision": "fp16", - "weights": "w8", - "bytes": 28377414 - }, - "duration_predictor": { - "precision": "fp16", - "weights": "w8", - "bytes": 10885059 - }, - "vector_context_encoder": { - "precision": "fp16", - "weights": "w8", - "bytes": 8029410 - }, - "vector_estimator": { - "precision": "fp16", - "weights": "w8", - "bytes": 63149207 - }, - "vocoder": { - "precision": "fp16", - "weights": "w8", - "bytes": 43811152 - } - }, - "compute_units": "gpu", - "neural_engine_assets": [ - "vector_estimator" - ] } }, "dataset_summary": { "controls": "measured_four_axes", - "voice_locale_pairs": 60 + "voice_locale_pairs": 84 }, 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